https://github.com/awslabs/gluonts - 0.16.2

What's Changed

Make observedvaluefield optional in TFTInstanceSplitter #3259 by @abdulfatir
Cap rpy2 version #3261 by @lostella

Full Changelog: https://github.com/awslabs/gluonts/compare/v0.16.1...v0.16.2

- Python
Published by lostella 8 months ago

https://github.com/awslabs/gluonts - v0.16.1

What's Changed

Allow disabling scaler for DL models #3251 by @shchur
Upgrade docs dependencies #3252 by @lostella
Set observed=True inside DataFrame.groupby in PandasDataset to silence the FutureWarning #3254 by @shchur

Full Changelog: https://github.com/awslabs/gluonts/compare/v0.16.0...v0.16.1

- Python
Published by lostella 11 months ago

https://github.com/awslabs/gluonts - 0.16.0

New features and enhancements

PatchTST: Add support for dynamic features by @rshyamsundar in https://github.com/awslabs/gluonts/pull/3167

Fixes

Fix freq string issues in datasets by @lostella in #3232

Documentation

Add model card from PR 2748 by @robert501128 in https://github.com/awslabs/gluonts/pull/3180

Dependencies updates

Bump pytorch lightning compat by @lostella in https://github.com/awslabs/gluonts/pull/3195
Update: Import of gluonts modules in pts module by @ClaraGrthns in https://github.com/awslabs/gluonts/pull/3194
Bump torch from 1.13.1 to 2.2.0 in /src/gluonts/nursery/daf by @dependabot in https://github.com/awslabs/gluonts/pull/3204
Upgrade dependencies in nursery by @lostella in https://github.com/awslabs/gluonts/pull/3206
Bump dependency version ranges for numpy & lightning by @shchur in https://github.com/awslabs/gluonts/pull/3226
Ignore cpflows dependencies in test workflows, fix numpy 2 incompatibilities. by @lostella in https://github.com/awslabs/gluonts/pull/3227
Update gluonts.shell requirements by @lostella in #3233

Testing

Drop Python 3.8 from workflows, run on 3.11 by @lostella in #3228

New Contributors

@robert501128 made their first contribution in https://github.com/awslabs/gluonts/pull/3180
@ClaraGrthns made their first contribution in https://github.com/awslabs/gluonts/pull/3194

Full Changelog: https://github.com/awslabs/gluonts/compare/v0.15.1...v0.16.0

- Python
Published by lostella over 1 year ago

https://github.com/awslabs/gluonts - 0.16.0rc1

- Python
Published by shchur over 1 year ago

https://github.com/awslabs/gluonts - 0.15.1

Backporting fixes: - Fix incorrect input routing for models #3186 (by @shchur) - Docs: fix custom pytorch model tutorial #3188 (by @lostella) - Enable QuantileOutput for TiDE model #3189 (by @shchur)

- Python
Published by lostella over 1 year ago

https://github.com/awslabs/gluonts - 0.15.0

New features

add iTransformer multivariate forecaster (#3017) by @kashif
Add TiDE model (#3096) by @maxc01
Add ercot dataset (#3148) by @lostella
Add ETT datasets (#3149) by @lostella
Add item_id to ERCOT and ETT datasets (#3150) by @shchur
Add Seasonal Aggregate Predictor (#3162) by @rshyamsundar

Model updates

Add option to provide function for season_length in SeasonalNaivePredictor (#3033) by @lostella
R: add ARIwith exogenous regressors (#3086) by @leica2023

Breaking changes

ev: Batch data for faster evaluation. (#3051) by @lostella
Unify QuantileOutput and DistributionOutput (#3093) by @shchur
Rotbaum: turn to json-based serialization (#3176) by @lostella

Fixes

Fix loader for the M5 dataset (#3151) by @shchur
Fix loaders for M5 & ETT datasets (#3155) by @shchur
Fix item_id for M5 dataset (#3156) by @shchur
Serde: limit decode code execution (#3175) by @lostella

Dependencies

Nursery: bump up pyarrow requirement in gluonts.nursery.tsbench (#3056) by @lostella
Bump scikit-learn from 0.23.2 to 1.0.1 in /src/gluonts/nursery/daf (#3118) by @dependabot
Bump sklearn version in nursery subpackages (#3120) by @lostella
Bump dependencies versions to address dependabot alerts (#3173) by @lostella
Bump dependencies versions to address dependabot alerts, again (#3174) by @lostella

Other changes

update dockerfile python version to 3.8 (#3072) by @melopeo
Update REFERENCES.md (#3078) by @abdulfatir
CI: run R tests on push (#3089) by @lostella
CI: chain requirements to ensure compatibility (#3112) by @Borda
test: unify using @pytest.mark.flaky(...) & bump pytest plugins (#3132) by @Borda
Add Chronos Breaking News (#3154) by @abdulfatir
lint: freeze & run Black version 24.02 (#3131) by @Borda
ci: update lints Ruff & docformatter (#3130) by @Borda
Extend test coverage for pandas frequencies (#3179) by @shchur
Tests: xfail test for fourier.arima.xreg (#3182) by @lostella

- Python
Published by lostella over 1 year ago

https://github.com/awslabs/gluonts - 0.14.4

Backporting fixes: - Fix type annotation for device in PyTorchPredictor #3094 by @lostella - extend exception for invalid offset_name #3115 by @Borda

- Python
Published by lostella about 2 years ago

https://github.com/awslabs/gluonts - 0.14.3

Backporting: - Fix Rotbaum serialization and deserialization #3068 by @pantanurag555 - Fix Rotbaum to handle short series #3073 by @leica2023

- Python
Published by lostella about 2 years ago

https://github.com/awslabs/gluonts - 0.13.9

Backporting: - Fix Rotbaum serialization and deserialization #3068 by @pantanurag555 - Fix Rotbaum to handle short series #3073 by @leica2023

- Python
Published by lostella about 2 years ago

https://github.com/awslabs/gluonts - 0.14.2

Backporting fixes: - Fix iterable.Cached. #3060 by @jaheba - Torch: Remove double caching of dataset. #3061 by @jaheba

- Python
Published by lostella about 2 years ago

https://github.com/awslabs/gluonts - 0.14.1

Backporting fixes: - Fix edge cases in metric computation #3037 (@lostella) - Refactor tests for ev.aggregations #3038 (@lostella) - Add plt.show() to README #3043 (@lostella) - Rotbaum: Add item-id to forecast. #3049 (@jaheba) - Fix mypy checks #3052 (@lostella)

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.8

Backporting fixes: - Fix edge cases in metric computation #3037 (@lostella) - Refactor tests for ev.aggregations #3038 (@lostella) - Rotbaum: Add item-id to forecast. #3049 (@jaheba) - Fix mypy checks #3052 (@lostella)

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.14.0

Release notes coming soon

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.14.0 rc2

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.7

Fixing issues with updated requirements, which would prevent the package from installing alongside old versions of PyTorch Lightning: - Fix lightning import (#3018 by @shchur) - Update requirements-pytorch.txt (#3019 by @shchur)

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.14.0 rc1

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.6

Backporting fixes: - [torch] Return a model even if callback has no best model path #2952 by @kashif - Move from pytorch_lightning to lightning #3013 by @shchur

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.5

Backporting fixes: - Raise warning in QuantileForecast.mean when mean is not there #2843 by @lostella - [R] Bug fix for running R methods via ParallelizedPredictor #2983 by @rshyamsundar - Fix Forecast plot method #3006 by @lostella

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.4

Backporting fixes: - Turn type comparison into isinstance #2958 by @lostella - Fix Cython version in XGBoost tests #2966 by @lostella - Clean up RepresentablePredictor #2967 by @lostella - Fix: use isinstance instead of type comparison #2973 by @melopeo
- Fix ArrowDecoder.decode to return instead of yield #2976 by @lostella - Unpin pyarrow version #2977 by @lostella

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.3

Backporting fixes: - Zebras: Fix index handling of SplitFrame.resize. #2938 by @jaheba - Docs: fix missing values use-case in tutorial for PandasDataset #2941 by @cneely33 - Ignore F403 errors in preludes. #2948 by @jaheba - Fix: prevent accumulation of SelectFields in PyTorchPredictor #2951 by @Cameronwood611 - [Docs] fix link to NPTS implementation #2953 by @lostella

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.2

Backporting fixes: - Fix NaNs in seasonal error #2894 by @gorold - Filter unused fields during inference #2905 by @abdulfatir - Define repr instead of str for PandasDataset #2906 by @lostella - Fix typo in background.md #2907 by @tnixon - Fix another typo in background.md #2908 by @tnixon

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.1

Backporting fixes: - Speedup is_uniform for PandasDataset. #2878 by @jaheba - Fix docstrings in torch lightning modules #2880 by @lostella - Fix default scale in torch DeepAR #2885 by @lostella

- Python
Published by lostella over 2 years ago

https://github.com/awslabs/gluonts - 0.13.0

Overview

We're happy to release gluonts version 0.13! This release contains a few new features and breaking changes compared to 0.12, especially around PyTorch models, data handling and model evaluation:

Added Support for PyTorch 2.0 , Lightning 2.0, Pandas 2.0.
New, more ergonomic model evaluation routines were added in #2673 (see the PR description for details on how to use those).
New PyTorch-based models, including PatchTST (#2748), and other torch-models-related breaking changes and improvements like #2603, #2614, #2628, #2688, and #2618.
Faster PandasDataset (#2663, #2665, #2860)

There are several more improvements and fixes compared to 0.12, which you can find in the changelog below. This release was possible thanks to the great work of several contributors: @jaheba, @MarcelK1102, @lostella, @gorold, @kashif, @dcmaddix, @abdulfatir, @melopeo, @huibinshen, @shchur, @pablovicente, @Gandor26, @Linbo-Liu. Thanks everyone, and thanks to users and authors of issue reports for the precious feedback!

Changelog

Breaking changes

Add transform.Valmap, improve transform.Chain. (#2629)
make Pytorch scaler's forward API consistent (#2627)
Remove torch specific Dataloader, remove num_workers from torch models. (#2628)
Pass prediction inputs as dict. (#2646)
Simplify scalers, move to gluonts.torch.scaler (#2632)
Remove TimeSeriesSlice (#2680)
Add model.Input. (#2684)
Remove FallbackPredictor. (#2686)
Move model init to lightning module. (#2688)
torch.SimpleFeedForward: Rename context to past_target. (#2704)
Fix style and type issues (#2711)
Fix off-by-one in torch DeepAR (#2618)
Remove dataset.Schema. (#2798)
Simplify univariate R wrapper (#2830)
Simplify plotting of forecasts. (#2864)

Major improvements or new features

Expose weight_decay in torch TFT estimator class (#2603)
Allow ReduceLROnPlateau to track val_loss when validation set is available (#2614)
Add wrapper for Nixtla/hierarchicalforecast (#2591)
Add zebras freq/period. (#2651)
Faster index building in PandasDataset (#2663)
Speed up PandasDataset.fromlongdataframe (#2665)
Add zebras.TimeFrame. (#2672)
Allow PyTorch 2.0 (#2724)
Fix Pandas 2.0 compatibility issues (#2710)
Allow PyTorch Lightning 2.0 (#2728)
Add itertools.PickleCache. (#2756)
Add Monash repository datasets (#2771)
Merge zebras from proof of concept branch. (#2776)
Add interface to gluonts.ev (#2673)
Zebras: Add from_pandas classmethods to TimeFrame and Periods. (#2807)
Zebras: Improve TimeFrame.split. (#2808)
Zebras: Add TimeFrame.rename. (#2810)
Zebras: Add TimeFrame.rolsplit. (#2809)
Add fourier.arima for long seasonal time series (#2789)
Add patch-TST, D-Linear and a new lag-TST model (#2748)

Minor improvements or new features

Rework torch MeanScaler. (#2600)
Add dataset.loader.asstackedbatches. (#2638)
Add Cyclic.stream. (#2639)
Add Wiki 2000 dataset (#2642)
Make hierarchicalforecast a single module. (#2666)
Add itertools.pluck_attr. (#2668)
Add itertools.power_set (#2682)
Allow axis to be a tuple in ev.aggregations (#2681)
Export torch models in torch module directly. (#2685)
Add zebras resize. (#2705)
improve comprehension list style (#2715)
Improve check for validation loop in lightning modules (#2726)
Add warning for "object" features in PandasDataset (#2731)
Remove usage of glide in tsf-reader. (#2737)
Add cdf and icdf to torch NegativeBinomial distribution (#2749)
Reduce depth of get_dataset import (#2796)
Reduce depth of gluonts.dataset.repository imports in code and docs (#2803)
Fix: remove multiprocessing from TSFReader (#2806)
Add maybe stub file. (#2811)
Add SizedIterableSlice, an IterableSlice that supports len() (#2815)
add validated to DeepNPTS (#2823)
Refactor base metrics computation in Evaluator class (#2825)
Remove second call to createlightningmodule on torch estimator (#2834)
Ingore hidden files in FileDataset by default. (#2847)
Add --compression argument to arrow writer cli. (#2848)
Zebras: handling of weekday offsets. (#2849)
Zebras: Add unix_epoch method to Period and Periods. (#2851)
Zebras: Improve equality for Periods. (#2857)
Add join_items to itertools. (#2859)
Zebras: Add eq_to to TimeFrame. (#2858)
Zebras: Add eq_shape to TimeFrame. (#2863)
Zebras: Allow to slice TimeFrame using plain strings for time info. (#2865)
Zebras: Add TimeSeries.to_numpy. (#2866)
Cache groupby result in PandasDataset (#2860)
Add featstaticcat for TSF datasets (#2871)

Bug fixes

Ensure dtype on feat_time in torch DeepAR. (#2596)
Add assertion to split function ensuring valid windows (#2587)
Fix bug with static cardinalities in PandasDataset (#2599)
Add gluonts.util.safe_extract (#2606)
Expose aggregation method in ensemble NBEATS, fix forecast shape (#2598)
Fix incorrect import in tsbench, apply latest black (#2613)
Fix: torch PoissonOutput scaling (#2619)
Remove dataclasses requirement (#2623)
Implement equals for initpassedkwargs. (#2630)
Fix bugs in MeanScaler (#2633)
Fix validation_data usage in torch. (#2643)
Fix norm-freq to consider freq starts. (#2645)
Fix call to extractall (#2648)
Delay instantiation of ScipyStudentT object (#2660)
Fix DateSplitter when split date is before start (#2670)
Remove creation of ragged sequences in MultivariateGrouper (#2671)
Fix ev.seasonal_error (#2696)
Fix zebras period time features. (#2700)
Update hierarchicalforecast for new release (#2709)
Fix DistributionForecast failure on GPU (#2714)
Fix version location for sdist. (#2729)
Fix validation loop check for Lightning modules (#2733)
Fix version cmdclass handling. (#2735)
Fix: use non-strict inequality in definition of coverage (#2738)
Fix MXNet NOPScaler (#2744)
Fix dataset file discovery. (#2777)
Fix: Loading of nested paths in FileDataset. (#2779)
Prophet: Pass 'item_id' and 'info' to forecast. (#2780)
Avoid zero scale in StudentTOutput (#2791)
Zebras: time length fixes. (#2799)
Remove .to_timestamp() to fix interval plotting (#2800)
Fix pd.Period serialization (#2827)
Fix torch DeepAREstimator in case context_length=1 (#2841)

Documentation

Fix installation docs, fix typos in docstrings (#2625)
Fix r-forecast doc strings (#2669)
Add doctests. (#2683)
Fix MultivariateEvaluator docstrings (#2693)
Docs: minor spelling fix (#2701)
Docs: Add extra requirements. (#2732)
Update Available Models (#2740)
Update REFERENCES.md (#2824)
Update plotting in readme example. (#2867)
Docs: Fix and simplify tutorials. (#2869)
Docs: Fix black formatting of % instructions. (#2870)
Docs: Add download link to notebooks. (#2872)
Docs: Use torch DeepAR in README. (#2874)

Test / setup changes

Fix version in requirements to comply with stricter setuptools. (#2604)
Test: Increase timeout for xgboost tests. (#2601)
Ignore warnings in tests. (#2620)
Test: Remove -v option from pytest. (#2631)
Add hierarchicalforecast to github workflows. (#2659)
Make nursery tests opt-in. (#2667)
Add test for torch models tracing (#2658)
Relax pandas requirement to include pandas 2.x. (#2713)
Update CP-Flow fork as extra dependency for MQF2 (#2727)
Add scipy requirement (#2745)
Fix pandas removed deprecations in tests (#2778)
Test: Set caplog level for shell tests. (#2786)
Bump numpy from 1.19.2 to 1.22.0 in /src/gluonts/nursery/daf (#2787)
Update setuptools and wheel in test workflow (#2802)
Bump torch from 1.6.0 to 1.13.1 in /src/gluonts/nursery/daf (#2788)
Add test workflow for R based models (#2814)
Add tests for hierarchical model to R workflow (#2819)
Move notebook compilation logic to docs workflow (#2831)
Fix bug in docs workflow (#2836)
Fix docs workflow further (#2837)
Fix string literal in docs workflow (#2839)
Update action to configure AWS credentials (#2873)

Others

Move NPTS back to gluonts.model (#2597)
Remove mxnet from default dataset path (#2635)
Roll back MQF2 import (#2687)
add DAF source code (#2769)
Add code for multivariate attack paper (#2697)
Update wiki2k tarball path (#2805)
[Nursery] CoP-DeepAR: Model for temporal hierarchical forecasting (#2812)
Cop deepar: Reset the no. of epochs to the default value (#2813)
Guard scripts execution in nursery (#2832)

- Python
Published by lostella almost 3 years ago

https://github.com/awslabs/gluonts - 0.13.0 rc1

- Python
Published by lostella almost 3 years ago

https://github.com/awslabs/gluonts - 0.12.8

Backporting fixes: - Remove .totimestamp() to fix interval plotting #2800 by @abdulfatir - Fix pd.Period serialization #2827 by @abdulfatir - Remove second call to createlightningmodule on torch estimator #2834 by @pablovicente - Fix torch DeepAREstimator in case contextlength=1 #2841 by @lostella - Ignore hidden files in FileDataset by default. #2847 by @jaheba

- Python
Published by lostella almost 3 years ago

https://github.com/awslabs/gluonts - 0.12.7

Backporting fixes: - Test: Set caplog level for shell tests. #2786 by @jaheba - Avoid zero scale in StudentTOutput #2791 by @shchur - Update setuptools and wheel in test workflow #2802 by @lostella - Fix: remove multiprocessing from TSFReader #2806 by @lostella

- Python
Published by lostella almost 3 years ago

https://github.com/awslabs/gluonts - 0.11.12

Backporting fixes:

Fix _version location for sdist. #2729 by @jaheba
Fix version cmdclass handling. #2735 by @jaheba
Remove usage of glide in tsf-reader. #2737 by @jaheba
Fix: use non-strict inequality in definition of coverage #2738 by @lostella
Fix MXNet NOPScaler #2744 by @abdulfatir
Add scipy requirement #2745 by @abdulfatir
Fix dataset file discovery. #2777 by @jaheba
Fix: Loading of nested paths in FileDataset. #2779 by @jaheba
Prophet: Pass 'item_id' and 'info' to forecast. #2780 by @jaheba
Test: Set caplog level for shell tests. #2786 by @jaheba

- Python
Published by lostella almost 3 years ago

https://github.com/awslabs/gluonts - 0.12.6

Backporting fixes: - Fix version cmdclass handling. #2735 by @jaheba - Remove usage of glide in tsf-reader. #2737 by @jaheba - Fix: use non-strict inequality in definition of coverage #2738 by @lostella - Update Available Models #2740 by @abdulfatir - Fix MXNet NOPScaler #2744 by @abdulfatir - Add scipy requirement #2745 by @abdulfatir - Fix dataset file discovery. #2777 by @jaheba - Fix pandas removed deprecations in tests #2778 by @lostella - Fix: Loading of nested paths in FileDataset. #2779 by @jaheba - Prophet: Pass 'item_id' and 'info' to forecast. #2780 by @jaheba

- Python
Published by lostella almost 3 years ago

https://github.com/awslabs/gluonts - 0.12.5

Backporting fixes: - Allow PyTorch 2.0 #2724 by @lostella - Improve check for validation loop in lightning modules #2726 by @lostella - Update CP-Flow fork as extra dependency for MQF2 #2727 by @lostella - Allow PyTorch Lightning 2.0 #2728 by @lostella - Fix _version location for sdist. #2729 by @jaheba - Add warning for "object" features in PandasDataset #2731 by @lostella - Docs: Add extra requirements. #2732 by @jaheba - Fix validation loop check for Lightning modules #2733 by @lostella

- Python
Published by lostella almost 3 years ago

https://github.com/awslabs/gluonts - 0.12.4

Backporting fixes: - Fix ev.seasonal_error #2696 by @lostella - Docs: minor spelling fix #2701 by @lostella - Fix Pandas 2.0 compatibility issues #2710 by @lostella - Relax pandas requirement to include pandas 2.x. #2713 by @jaheba - Fix DistributionForecast failure on GPU #2714 by @huibinshen

- Python
Published by melopeo almost 3 years ago

https://github.com/awslabs/gluonts - 0.12.3

Backporting fixes: - Delay instantiation of ScipyStudentT object #2660 by @gorold - Faster index building in PandasDataset #2663 by @huibinshen - Speed up PandasDataset.fromlongdataframe #2665 by @lostella - Fix r-forecast doc strings #2669 by @abdulfatir - Fix DateSplitter when split date is before start #2670 by @gorold - Remove creation of ragged sequences in MultivariateGrouper #2671 by @abdulfatir

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.11

Backporting fixes: - Faster index building in PandasDataset #2663 by @huibinshen - Speed up PandasDataset.fromlongdataframe #2665 by @lostella - Fix DateSplitter when split date is before start #2670 by @gorold - Remove creation of ragged sequences in MultivariateGrouper #2671 by @abdulfatir

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.12.2

Backporting fixes: - Fix PyTorch training loop #2643 by @gorold - Fix norm-freq to consider freq starts. #2645 by @jaheba - Fix call to extractall #2648 by @lostella

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.10

Backporting fixes: - Fix PyTorch training loop #2643 - Fix norm-freq to consider freq starts. #2645 - Fix call to extractall #2648

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.10.10

Backporting fix: - Fix call to extractall #2648

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.9.10

Backporting fixes: - Cap numpy compatibility in mxnet extra requirements #2506 - Add gluonts.util.safe_extract #2606 - Fix call to extractall #2648

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.12.1

Backporting fixes: - Fix: torch PoissonOutput scaling #2619 by @kashif - Remove dataclasses requirement #2623 by @lostella - Fix installation docs, fix typos in docstrings #2625 by @lostella

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.10.9

What's Changed

Backports for v0.10.9 by @jaheba in https://github.com/awslabs/gluonts/pull/2610

Backporting fixes - #2598 - #2606 - #2604

Full Changelog: https://github.com/awslabs/gluonts/compare/v0.10.8...v0.10.9

- Python
Published by jaheba about 3 years ago

https://github.com/awslabs/gluonts - 0.12.0

Overview

Support for Python 3.6 is dropped (#2542).

Models:

Added PyTorch implementation of the Temporal Fusion Transformer model (#2536)
Various improvements to PyTorch DeepAR (#2433, #2476, #2545, #2552, #2553, #2556, #2596)
Added wrappers for statsforecast models (#2360, #2515, #2561)
Added wrappers for hierarchical time series models in R (#2396, #2406, #2412)
Updated R wrappers and dockerfile (#2571, #2572)
Important the Naive2, Rforecast, Prophet, and Rotbaum models have been moved to gluonts.ext (#2362, #2597)

Data:

Improved PandasDataset: allows specifying static features as a separate dataframe, instead of watefully replicate feature values over time. This was particularly problematic in large datasets, such as M5. In the new setup, static features are provided via a separate dataframe indexed by item_id, and the dtype of each column determinins which are numerical vs categorical features, with automated detection of cardinalities in the latter case. See the updated tutorial notebook on how to use it.

Evaluation:

New evaluation module gluonts.ev (#2450) will gradually replace the existing gluonts.evaluation as an improved, more flexible alternative.

Changelog

Breaking changes

Remove folders of models that have moved to mx.model (#2356) @codingWhale13
Remove model.common. (#2358) @jaheba
Remove dataset.rolling_dataset. (#2361) @jaheba
Remove DummyEstimator. (#2357) @jaheba
Introduce gluonts.ext. (#2362) @jaheba
Make serde.dataclass always kw-only. (#2428) @jaheba
Add copy_dim to QuantileForecast, change dim method for multivariate data (#2352) @codingWhale13
Include loss computation in torch DeepAR module, decouple MQF2 (#2476) @lostella
Remove serde dumpcode/loadcode. (#2482) @jaheba
Move SelfAttentionEstimator to gluonts.nursery (#2534) @lostella
Require python 3.7. (#2542) @jaheba
Simplify forecast.Quantile. (#2544) @jaheba
Move shell related forecast classes to shell. (#2547) @jaheba
Consolidate DeepNPTSEstimator (#2496) @lostella
Improve PandasDataset (#2573) @lostella @jaheba
Simplify PandasDataset. (#2583) @jaheba

Major improvements / new features

Add dict like interface for Forecast. (#2384) @jaheba
Enable dropping of columns in dataset.schema.translate. (#2387) @jaheba
Exposing the choice of trainsampler and validationsampler for MQCNN and MQRNN (#2381) @sighellan
Add wrapper for statsforecast models (#2360) @lostella
Add dataset.schema.Schema + types. (#2391) @jaheba
Add IQN implementation (#1784) @kashif
Add hierarchical time series reconciliation methods from R/hts: top-down and middle-out (#2396) @melopeo
Add hierarchical time series reconciliation method from R/hts: MinT (#2406) @melopeo
Change schema.Type to behave like invokable types. (#2443) @jaheba
Add cdf and icdf methods for StudentT distribution (#2439) @shchur
Better DeepAR lags for business day frequency time series. (#2433) @sighellan
Add support for feather; incl compression. (#2452) @jaheba
Introduce ev module (#2450) @codingWhale13 @jaheba
Speed up PandasDataset further (#2441) @lostella
Add Empirical Risk Minimzation (ERM) hierarchical forecasting method (#2412) @melopeo
Update statsforecast model wrappers (#2515) @lostella
Add nan values and explainability support for rotbaum (#2537) @zoolhasson
Enable setting a custom imputation method in deepar pytorch (#2545) @shubhamkapoor
Add deriveautofields for DeepAR PyTorch (#2552) @shubhamkapoor
Add default_scale to MeanScaler and enable the option in DeepAR-PyTorch (#2553) @shubhamkapoor
Add statsforecasts models (#2561) @melopeo
Add TemporalFusionTransformer implementation in PyTorch (#2536) @shchur
Fix r_forecast methods to work with rpy2 v3+ (#2571) @abdulfatir
Updated dockerfile for R forecast models (#2572) @abdulfatir
Shell: Add support for requirements.txt files. (#2582) @jaheba
Expose weight_decay in torch TFT estimator class (#2603) @gorold
Allow ReduceLROnPlateau to track val_loss when validation set is available (#2614) @gorold

Minor improvements / new features

Expose SampleForecast, QuantileForecat directly in model. (#2366) @jaheba
Mypy fixes (#2427) @jaheba
Add nursery.pipeline. (#2429) @jaheba
itertools.select. (#2426) @jaheba
Add itertools.Filter. (#2438) @jaheba
Add itertools.trim_nans. (#2460) @jaheba
Add itertools.inverse. (#2463) @jaheba
Fix: sort dataset keys in error message when importing non-existing dataset (#2497) @lostella
Few shot forecasting (#2517) @RingoIngo
Allow passing of additional args to dataclass. (#2531) @jaheba
Simplify linear interpolation in forecast.py (#2546) @jaheba
Add util.lazy_property. (#2557) @jaheba
Compact PandasDataset string representation (#2558) @lostella
Add default args and assertions to DeepAR pytorch module, assertions (#2556) @lostella
Update MANIFEST.in. (#2566) @jaheba
Add util.copy_with. (#2562) @jaheba
Add missing value imputation to Seasonal Naive (#2569) @abdulfatir
Implement get-item for JsonLinesFile. (#2574) @jaheba
Make itertools Map/Filter dataclasses. (#2579) @jaheba
Add itertools.StarMap. (#2584) @jaheba
Add gluonts.maybe. (#2585) @jaheba
Rework maybe. (#2593) @jaheba

Bug fixes

Fix dominick dataset bug. (#2364) @haskarb
Proposed fix to zero seed bug. (#2379) @sighellan
Fix rotbaum random seed and num_samples argument. (#2408) @sighellan
Removed unused import in test.(#2409) @kashif
Hierarchical: Make sure the input S matrix is of right dtype (#2415) @rshyamsundar
Speed up PandasDataset for long dataframes (#2435) @lostella
Fix frequency inference in PandasDataset (#2442) @lostella
Fix plotting date index bug in anomaly detection example (#2446) @Amrit-Bhaskar-abhask10
Add test cases for PandasDataset, fix missing assertion (#2453) @lostella
Fix MANIFEST.in (#2456) @lostella
Fix pandas issue with inferring start of X frequency. (#2462) @jaheba
Change default forecast_type of ND metric to median (#2472) @codingWhale13
Fix: use right context in DeepVARHierarchicalEstimator (#2478) @c3-ziqin
Add requirement files to MANIFEST.in (#2490) @jaheba
Fix dataclass handling of member inheritance. (#2492) @jaheba
Fix DateSplitter for multiples of base frequencies (#2500) @lostella
Fix serde.dataclass inheritance handling. (#2512) @jaheba
Fix QuantileForecast.quantile in case only mean is stored (#2513) @lostella
Fix aggregate_valid for non-numerical columns (#2526) @lostella
Fix dataclass eventual handling. (#2530) @jaheba
Change SeasonalNaive fallback predictor to nanmean (#2549) @abdulfatir
Fix: add missing params in rotbaum (#2554) @zoolhasson
Add NaN validation to Evaluator (#2568) @abdulfatir
Fix: avoid automatic device detection via serialized tensors when deserializing (#2576) @shubhamkapoor
serde: Fix encoding of dtypes. (#2586) @jaheba
Fix bug with static features in PandasDataset (#2589) @lostella
Fix maybe mapor/mapor_else return types. (#2588) @jaheba
Add assertion to split function ensuring valid windows (#2587) @MarcelK1102
Ensure dtype on feat_time in torch DeepAR. (#2596) @jaheba
Expose aggregation method in ensemble NBEATS, fix forecast shape (#2598) @lostella
Fix bug with static cardinalities in PandasDataset (#2599) @lostella
Add gluonts.util.safe_extract (#2606) @lostella
Fix incorrect import in tsbench, apply latest black (#2613) @lostella

Documentation

Udpate DeepAR import in README. (#2359) @codingWhale13
Remove strange quoting marks from docstrings (#2368) @lostella
Change 'confidence interval' to 'prediction interval' (#2373) @codingWhale13
Fix use of dump_code in tutorial. (#2488) @jaheba
Fix docstrings according to docformatter (#2501) @lostella
Docs: Fix install instructions. (#2508) @jaheba
Add examples to docstring for periods_between (#2504) @lostella
Add info on how versioning works. (#2529) @jaheba
Improve README example (#2538) @lostella
Update REFERENCES.md @dcmaddix

Test / setup changes

Update workflow actions to latest versions (#2447) @lostella
Tests: Change Python versions. (#2448) @jaheba
Use ruff instead of flake8. (#2485) @jaheba
Apply ruff/pyupgrade to test. (#2489) @jaheba
Add smoke tests for torch models (#2495) @lostella
Pin docformatter version. (#2507) @jaheba
Cap numpy compatibility in mxnet extra requirements (#2506) @lostella
Clean up test code for evaluator (#2505) @lostella
Remove mypy plugin for dataclass. (#2514) @jaheba
GH Actions: Use authenticated requests for just. (#2522) @jaheba
Simplify setup.py (#2525) @jaheba
Test: Only check relevant require-packages.txt for test run. (#2595) @jahaba
Fix version in requirements to comply with stricter setuptools. (#2604) @lostella

Other

Move NPTS back to gluonts.model (#2597) @lostella

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.9

Backporting fixes: - Fix: avoid automatic device detection via serialized tensors when deserializing PyTorchPredictor. #2576 by @shubhamkapoor - Fix Map representation. #2579 by @jaheba - serde: Fix encoding of dtypes. #2586 by @jaheba - Add assertion to split function ensuring valid windows #2587 by @MarcelK1102 - Ensure dtype on feattime in torch DeepAR. #2596 by @jaheba - Expose aggregation method in ensemble NBEATS, fix forecast shape #2598 by @lostella - Fix requirements following breaking change in setuptools #2604 by @lostella - Add gluonts.util.safeextract #2606 by @lostella

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.12.0 rc1

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.8

Backporting fixes: - Update workflow actions to latest versions #2447 by @jaheba - Simplify setup.py #2525 by @jaheba - Fix dataclass eventual handling. #2530 by @jaheba - Improve README example #2538 by @lostella - Change SeasonalNaive fallback predictor to nanmean #2549 by @abdulfatir - Compact PandasDataset string representation #2558 by @lostella - Update MANIFEST.in. #2566 by @jaheba - Add NaN validation to Evaluator #2568 by @abdulfatir

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.7

Backporting fixes: - Make serde.dataclass always kw-only. (#2428 by @jaheba) - Fix serde.dataclass inheritance handling. (#2512 by @jaheba) - Fix QuantileForecast.quantile in case only mean is stored (#2513 by @lostella) - Remove mypy plugin for dataclass. (#2514 by @jaheba) - GH Actions: Use authenticated requests for just. (#2522 by @jaheba) - Fix aggregate_valid for non-numerical columns (#2526 by @lostella)

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.6

Backporting fixes: - itertools.select. #2426 by @jaheba - Fix dataclass handling of member inheritance. #2492 by @jaheba - Fix: sort dataset keys in error message when importing non-existing dataset #2497 by @lostella - Fix DateSplitter for multiples of base frequencies #2500 by @lostella - Fix docstrings according to docformatter #2501 by @lostella - Add examples to docstring for periods_between #2504 by @lostella - Cap numpy compatibility in mxnet extra requirements #2506 by @lostella - Pin docformatter version. #2507 by @jaheba - Docs: Fix install instructions. #2508 by @jaheba

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.5

What's Changed

Backports for v0.11.5. by @jaheba in https://github.com/awslabs/gluonts/pull/2491

Full Changelog: https://github.com/awslabs/gluonts/compare/v0.11.4...v0.11.5

- Python
Published by jaheba about 3 years ago

https://github.com/awslabs/gluonts - 0.11.4

Backports:

Fix pandas issue with inferring start of X frequency. (#2462 by @jaheba)

- Python
Published by jaheba about 3 years ago

https://github.com/awslabs/gluonts - 0.11.3

Backporting fixes: - Add test cases for PandasDataset, fix missing assertion (#2453 by @lostella) - Speed up PandasDataset further (#2441 by @lostella) - Fix MANIFEST.in (#2456 by @lostella)

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.3 rc1

Backporting fixes: - Add test cases for PandasDataset, fix missing assertion (#2453 by @lostella) - Speed up PandasDataset further (#2441 by @lostella) - Fix MANIFEST.in (#2456 by @lostella)

- Python
Published by lostella about 3 years ago

https://github.com/awslabs/gluonts - 0.11.2

Backporting fixes: - Fix rotbaum random seed and num_samples argument. (#2408 by @sighellan) - Hierarchical: Make sure the input S matrix is of right dtype (#2415 by @rshyamsundar) - Mypy fixes (#2427 by @jaheba) - Speed up PandasDataset for long dataframes (#2435 by @lostella) - Fix frequency inference in PandasDataset (#2442 by @lostella) - Tests: Change Python versions. (#2448 by @jaheba)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.11.1

Backporting fixes: - Fix dominick dataset bug. (#2364 by @haskarb) - Remove strange quoting marks from docstrings (#2368 by @lostella) - Consistent use of term "prediction interval" (#2373 by @codingWhale13) - Fix MQCMM ignoring zero-seed. (#2379 by @sighellan)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.8

Backporting fixes: - Fix numerical bug in BinnedUniforms (#2344 by @moudheus) - Fix dominick dataset bug. (#2364 by @haskarb) - Fix MQCMM ignoring zero-seed. (#2379 by @sighellan)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.11.0

Overview

Incremental training

Estimators are now re-trainable on new data, using the train_from method. This accepts a previously trained model (predictor), and new data to train on, and can greatly reduce training time if combined with early stopping. The feature is integrated with gluonts.shell-based SageMaker containers, and can be used by specifying the additional model channel to point to the output of a previous training job. More info in #2249.

New models

Two models are added in this release: * DeepVARHierarchicalEstimator, a hierarchical extension to DeepVAREstimator; learn more about how to use this in this tutorial. * DeepNPTSEstimator, a global extension to NPTS, where sampling probabilities are learned from data; learn more on how to use this estimator here.

Deprecated import paths and options

This release moves MXNet-based models from gluonts.model to gluonts.mx.model; the old import paths continue working in this release, but are deprecated and will be removed in the next release. For example, now the MXNet-based DeepAREstimator should be imported from gluonts.mx (or gluonts.mx.model.deepar).

We also removed deprecated options for learning rate reduction in the gluonts.mx.Trainer class: these can now be controlled via the LearningRateReduction callback.

Dataset splitting functionality (experimental)

We updated the functionality to split time series datasets (along the time axis) for training/validation/test purposes. Now this functionality can be easily accessed via the split function (from gluonts.dataset.split import split); learn more about this here.

This feature is experimental and subject to future changes.

Changelog

Breaking changes

Breaking: Update data splitters to return (input, output) pairs in the test split (#2031 by @npnv)
Breaking: Move MXNet-based models to mx.model. (#2126 by @Hongqing-work)
Convert time-features into functions. (#2149 by @jaheba)
Remove deprecated args from mx.Trainer. (#2153 by @jaheba)
Reduce sdist size. (#2199 by @jaheba)
Remove core.exception module. (#2202 by @jaheba)
Remove core.ty. (#2203 by @jaheba)
Update gluonts.dataset.split code, test, docs (#2223 by @lostella)
Remove gluonts_forecasters entrypoint mechanic. (#2278 by @jaheba)
Enable 'python -m gluonts'. (#2292 by @jaheba)

New features / major improvements

Interrupting mx.Trainer stops training. (#2131 by @Hongqing-work)
Expose evaluator aggregation_strategy functions (#2198 by @kashif)
Add data preparation utility for hierarchical time series and a tutorial notebook (#2206 by @rshyamsundar)
Add Deep NPTS model (#1835 by @rshyamsundar)
Improve arrow reading performance. (#2217 by @mr-1993)
Allow DeepVAR model to use (global) dynamic features (#2226 by @rshyamsundar)
Hierarchical: Allow use of external dynamic features and add a section in the tutorial (#2253 by @rshyamsundar)
Add serde.dataclass. (#2166 by @jaheba)
R: Add Python wrapper for calling R's hierarchical methods (#1685 by @rshyamsundar)
Add learning rate and weight decay arguments to PyTorch estimators (#2289)
Added LR scheduler to DeepAR Pytorch (#2287 by @shubhamkapoor)
Add LR scheduling patience option to MQF2 (#2291 by @lostella)
Add incremental training (#2249 by @lostella)
Add input size and type information to DeepARModel, and exampleinputarray to DeepARLightningModule. (#2307 by @jgasthaus)
Add dataset.schema.translate. (#2304 by @jaheba)
Add forecast_start to entry-wise metrics in evaluator (#2312 by @lostella)

Bug fixes / minor improvements

Fix DatasetCollection (#2135 by @rsnirwan)
Fix PandasDataset for Python 3.9 (#2141 by @lostella)
Make PandasDataset faster (#2148 by @lostella)
Ignore divide warnings in evaluation. (#2159 by @jaheba)
Fix Prophet wrapper to work with Timestamp instead of Period (#2182 by @lostella)
Fix dtype for "item_id" column in metrics dataframe (#2183 by @lostella)
Fix recursive case for gluonts.mx.batchify.stack (#2184 by @lostella)
Fix item_id values in ConstantValuePredictor (#2192 by @codingWhale13)
Fixup Patience class. (#2197 by @jaheba)
Fix dataset arrow writer tool. (#2196 by @jaheba)
Fix SymbolBlock serde issue (#2187 by @lostella)
Add item id to Uber TLC dataset (#2214 by @mvanness354)
Fix r_forecast wrapper to shift start date when truncating time series (#2216 by @abdulfatir)
Fix dtype bug in piecewise_linear and add a test (#2224 by @rshyamsundar)
Fix bug in to_quantile_forecast (#2225 by @eugeneteoh)
Fix gluonts.mx.trainer.Trainer in case of empty data loader (#2228 by @lostella)
Fix feed-forward models when features are provided (#2238 by @lostella)
update SplicedBinnedPareto demos from nursery version to gluonts version (#2250 by @elenaehrlich)
Improve len() for ParquetFile. (#2261 by @jaheba)
Move maxidletransform usage to GluonEstimator. (#2262 by @jaheba)
Optimize TimeSeriesSlice performance (#2259 by @lostella)
Fix ignore hidden files when generating datasets (#2263 by @kashif)
Fix: set max idle transforms in PyTorch estimators (#2266 by @lostella)
Fix QuantileForecast.plot() to use DateTimeIndex (#2269 by @abdulfatir)
Fix serde dataclass eventual. (#2277 by @jaheba)
Fix gluonts.dataset.split for multivariate case (#2314 by @lostella)
Improve TestData class in gluonts.dataset.split (#2315 by @lostella)
Simplify make_evaluation_predictions (#2309 by @lostella)
Fix MQCNN for kernel_size=1 (#2321 by @lostella)
Simplify unbatching in forecast-generator. (#2334 by @jaheba)
Fix numerical bug in BinnedUniforms (#2344 by @moudheus)

Documentation

Docs: Make notebook templates. (#2122 by @jaheba)
Docs: Rework installation section. (#2130 by @jaheba)
Docs: Fix running tutorials for publishing docs. (#2138 by @jaheba)
Docs: Update hyperparameter tuning with optuna notebook. (#2137 by @npnv)
Fix issues with hyperparameter tuning tutorial (#2143 by @lostella)
Apply black to notebooks. (#2144 by @jaheba)
Docs: Simplify wide DataFrame example (#2150 by @lostella)
Docs: fix links in models table (#2156 by @lostella)
Add 'Background' section to docs. (#2129 by @jaheba)
Docs: Add info about version guarantees. (#2161 by @jaheba)
Docs: fix tutorial after breaking changes in trainer class (#2179 by @lostella)
Add tutorial with data splitting examples (#2157 by @npnv)
Fix: add missing link to splitting tutorial (#2185 by @lostella)
Fix: ensure last cell of tutorials runs (#2186 by @lostella)
Fixes to the dataset splitting tutorial (#2189 by @npnv)
Update TSBench readme with paper reference (#2191 by @geoalgo)
Update Available models table with the hierarchical model (#2209 by @rshyamsundar)
Fix broken links in Available-models table (#2211 by @rshyamsundar)
Add logo to README. (#2248 by @jaheba)
New logo. (#2243 by @jaheba)
Use brand colors in docs. (#2257 by @jaheba)
Docs: Reformatting table, badge colors. (#2258 by @jaheba)
Docs: update contribution guidelines and dev setup (#2270 by @lostella)
Add Github footer icon to docs. (#2285 by @jaheba)
Docs: Custom Pygments style for dark theme. (#2290 by @jaheba)
Fix README quick examples (#2297 by @lostella)
Fix text in Quick Start Tutorial (#2300 by @sighellan)
Update README and tutorial (#2311 by @lostella)
Turn on apidoc generation (#2332 by @jaheba)
Add info on how to use 'just' (#2339 by @codingWhale13)
Small documentation improvements (#2343 by @codingWhale13)

Test / setup changes

add python 3.9 to test workflows (#2136)
Tests: Move mx model test. (#2158 by @jaheba)
Test: Use spawn method for shell server tests. (#2177 by @jaheba)
Remove holidays and matplotlib from core dependencies. (#2055 by @jaheba)
Update minimal version for nbconvert. (#2233 by @jaheba)
Hierarchical: Add a test for to_dataset method (#2265 by @rshyamsundar)
Fix mypy and black commands in pre-commit githook (#2271 by @abdulfatir)
Update project_urls. (#2274 by @jaheba)
Move _version to meta. (#2293 by @jaheba)
Remove setup-requires. (#2295 by @jaheba)
Remove pytest.ini. (#2298 by @jaheba)
Speed up smoke tests (#2341 by @lostella)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.7

Backporting fixes: - Add Github footer icon to docs. (#2285 by @jaheba) - Docs: Custom Pygments style for dark theme. (#2290 by @jaheba) - Fix README quick examples (#2297 by @lostella) - Fix text in Quick Start Tutorial (#2300 by @sighellan) - Update README and tutorial (#2311 by @lostella) - Fix MQCNN for kernel_size=1 (#2321 by @lostella)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.6

Backporting fixes: - Improve len() for ParquetFile. (#2261 by @jaheba) - Max idle transform fix (#2262 by @jaheba) - Fix ignore hidden files when generating datasets (#2263 by @kashif) - Fix: set max idle transforms in PyTorch estimators (#2266 by @lostella) - Fix QuantileForecast.plot() to use DateTimeIndex (#2269 by @abdulfatir)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.5

Backporting fixes: * Fix broken links in Available-models table (#2211 by @rshyamsundar) * Fix rforecast wrapper to shift start date when truncating time series (#2216 by @abdulfatir) * Improve arrow reading performance (#2217 by @mr-1993) * Fix dtype bug in piecewiselinear and add a test (#2224 by @rshyamsundar) * Fix bug in toquantileforecast (#2225 by @eugeneteoh) * Fix gluonts.mx.trainer.Trainer in case of empty data loader (#2228 by @lostella) * Fix feed-forward models when features are provided (#2238 by @lostella)

Full changelog: https://github.com/awslabs/gluon-ts/compare/v0.10.4...v0.10.5

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.9.9

Backporting fixes: * Fix rforecast wrapper to shift start date when truncating time series (#2216 by @abdulfatir) * Fix dtype bug in piecewiselinear and add a test (#2224 by @rshyamsundar) * Fix bug in toquantileforecast (#2225 by @eugeneteoh) * Fix gluonts.mx.trainer.Trainer in case of empty data loader (#2228 by @lostella) * Fix feed-forward models when features are provided (#2238 by @lostella)

Full Changelog: https://github.com/awslabs/gluon-ts/compare/v0.9.8...v0.9.9

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.4

Backporting fixes: * Fix SymbolBlock serde issue (#2187 by @lostella) * Fix dataset arrow writer tool. (#2196 by @jaheba) * Expose evaluator aggregation_strategy functions (#2198 by @kashif) * Update Available models table with the hierarchical model (#2209 by @rshyamsundar)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.9.8

Backporting fixes: - Fix SymbolBlock serde issue (#2187 by @lostella)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.3

Backporting fixes: - Fix Prophet wrapper to work with Timestamp instead of Period (#2182 by @lostella) - Fix dtype for "itemid" column in metrics dataframe (#2183 by @lostella) - Fix recursive case for gluonts.mx.batchify.stack (#2184 by @lostella) - Fix: ensure last cell of tutorials runs (#2186 by @lostella) - Fix itemid values in ConstantValuePredictor (#2192 by @codingWhale13)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.9.7

Backporting fixes: - Fix dtype for "itemid" column in metrics dataframe (https://github.com/awslabs/gluon-ts/pull/2183 by @lostella) - Fix recursive case for gluonts.mx.batchify.stack (https://github.com/awslabs/gluon-ts/pull/2184 by @lostella) - Fix itemid values in ConstantValuePredictor (https://github.com/awslabs/gluon-ts/pull/2192 by @codingWhale13)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.2

Backport fixes:

Make PandasDataset faster (#2148 by @lostella)
Interrupting mx.Trainer stops training. (#2131 by @Hongqing-work)
Ignore divide warnings in evaluation. (#2159 by @jaheba)

- Python
Published by jaheba over 3 years ago

https://github.com/awslabs/gluonts - 0.10.1

Backporting fixes: - Docs: Make notebook templates. (#2122 by @jaheba) - Docs: Rework installation section. (#2130 by @jaheba) - Fix DatasetCollection for Python 3.9. (#2135 by @rsnirwan) - Docs: Fix running tutorials for publishing docs. (#2138 by @jaheba) - Fix PandasDataset for Python 3.9 (#2141 by @lostella) - Fix issues with hyperparameter tuning tutorial (#2143 by @lostella) - Docs: Apply black to notebooks. (#2144 by @jaheba)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.0

Overview

Arrow based datasets

We have added support for Parquet-files, as well as Arrow's binary format. This is an opt-in feature, requiring pyarrow to be installed. Use pip install 'gluonts[pro]' or pip install 'gluonts[arrow]' to ensure the correct version is installed.

FileDataset has been reworked to support .parquet and .arrow files. Previously, it had assumed all files to use jsonlines. To continue using jsonlines ensure that the the files use one of the .json, .jsonl, .json.gz, jsonl.gz suffixes.

Depending on the dataset size and shape, Arrow can be much faster than the json variant. In more extreme cases we saw speedups of more than 100x when using arrow vs jsonlines (see #2003 for some examples).

To convert a given dataset into arrow, you can use the gluonts.dataset.arrow utility:

sh python -m gluonts.dataset.arrow write </path/to/dataset> my-dataset.arrow`

`PandasDataset`

We have added support for pandas.DataFrame and pandas.Series as well. You can now directly model data given in a DataFrame using gluonts.dataset.pandas.PandasDataset. In this tutorial we describe in depth how you can use PandasDataset to speed up modelling using GluonTS.

Changelog

New Features

#1631 - Add TimeLimitCallback to mx/trainer callbacks. (by @yx1215)
#1780 - adding MQF2 (Multi-horizon) (by @KelvinKan)
#1903 - Added QuarterlyBegin time feature (by @kashif)
#1924 - Porting SimpleFeedForwardEstimator to PyTorch (by @lostella)
#1925 - DeepAR PyTorch: make samplers configurable (by @lostella)
#1935 - added support for pandas dataframes (by @rsnirwan)
#1962 - Add support for beta-NLL loss (by @kashif)
#1982 - Add Uber-TLC dataset to dataset repository. (by @Hongqing-work)
#1990 - Add info cli. (by @jaheba)
#1987 - Add HP tuning example with Optuna (by @npnv)
#2000 - Add arrow-based dataset. (by @vafl, @lostella, @jaheba)
#2002 - add ND for item_metrics (by @melopeo)
#2006 - Added support of "long" RTS, making short RTS be "pastfeatdynamic_real" (by @zoolhasson)
#2061 - Add DatasetWriter. (by @jaheba)
#2074 - Add support for second frequency. (by @kashif)

Breaking Changes

#1917 - Breaking: Fix return types of features (by @lostella)
#1941 - Breaking: Update dependency fbprophet -> prophet (by @lostella)
#1946 - Breaking: Split incremental quantile output into separate class (by @lostella)
#1965 - Breaking: reorg torch package, shorten import paths (by @lostella)
#1980 - Use pd.Period instead of pd.Timestamp. (by @jaheba)
#1997 - Remove freq argument from Forecast. (by @kashif)
#2011 - Remove dct_reduce. (by @jaheba)
#2017 - Remove mandatory freq attribute of Predictor. (by @kashif)
#2018 - Remove multiprocessing dataloader. (by @jaheba)
#2019 - Rework FileDataset. (by @jaheba)
#2053 - Add dataset_writer to get_dataset. (by @Hongqing-work)
#2070 - Add jsonl.encode_json, remove serialize_data_entry. (by @jaheba)

Bug Fixes / Minor Improvements

#1704 - Settings._let will pop element it added instead of just the last one. (by @jaheba)
#1905 - Fix typing issues in torch estimators, update base estimators docstrings (by @lostella)
#1909 - Fix the use of the scaling parameter in Transformer model (by @StanislasGuinel)
#1916 - Fix AddTimeFeatures transformation for multiples of base frequencies (by @lostella)
#1920 - Fix: use broadcast_lesser in place of comparisons in ISQF (by @vincentqb)
#1931 - Fix dummy estimator (by @canerturkmen)
#1933 - Fix Pytorch Lightning tutorial. (by @jaheba)
#1938 - Fixed autograd inplace operations error in Transformed Distribution (by @shubhamkapoor)
#1950 - Fix: Hard threshold positive distribution parameters (by @lostella)
#1952 - Fix forecast keys (quantiles) output by TemporalFusionTransformer (by @lostella)
#1968 - Fix: use of numparallelsamples in deepAR (by @kashif)
#1969 - Fix: torch DeepAR observed indicator in multivariate case (by @kashif)
#1975 - use FieldName (by @kashif)
#1983 - Documentation: add docstrings for torch-based models (by @lostella)
#1986 - Fix OffsetSplitter for negative offsets (by @lostella)
#1989 - Pin protobuf version. (by @jaheba)
#1991 - Remove packaged pytorch-ts from gluonts.nursery.SCott (by @lostella)
#1999 - Documentation: fix and speed up tutorials (by @lostella)
#2004 - Refactor splitter assertion and add error message (by @RSNirwan)
#2005 - Rework itertools, add col-to-row and row-to-col functions. (by @jaheba)
#2008 - Re-add cache for parsing 'pd.Period'. (by @jaheba)
#2013 - Update website template, clean up homepage and tutorials (by @lostella)
#2014 - Expose Estimator, Predictor, Forecast in gluonts.model. (by @jaheba)
#2015 - Fix mean in AffineTransformedDistribution (by @stailx)
#2016 - Fix torch affine transformed distribution (by @lostella)
#2020 - Remove unnecessary files from docs folder, update gitignore (by @lostella)
#2021 - Update references to dev branch. (by @lostella)
#2024 - Fix README. Use DataFramesDataset. (by @jaheba)
#2025 - Make HP tuning tutorial more accurate (by @jaheba)
#2028 - Re-add support for Python 3.6 (by @jaheba)
#2029 - Add support for nan values in Rotbaum (by @zoolhasson)
#2035 - Simplify lag values computation in torch DeepAR (by @lostella)
#2036 - Minor improvements to the hierarchical model (by @rshyamsundar)
#2047 - Make Quantile derive from pydantic.BaseModel. (by @jaheba)
#2050 - Add concepts section to docs. (by @jaheba)
#2051 - Add tutorial on DataFramesDataset (by @RSNirwan)
#2057 - Add optional parameter time_axis to forecast_start. (by @melopeo)
#2062 - Fix type annotations for predict_to_numpy (by @lostella)
#2066 - Always pass freq explicitly to pd.period_range. (by @kashif)
#2068 - Docs: simplify call to evaluator (by @lostella)
#2092 - Fix: DistributionLoss not encodable. (by @jaheba)
#2098 - Add Airtraffic dataset. (by @jaheba)
#2108 - Fixup trainer in case of non-finite loss. (by @jaheba)
#2121 - Change default behavior for TrainDatasets overwrite (by @nklingen)

- Python
Published by jaheba over 3 years ago

https://github.com/awslabs/gluonts - 0.9.6

Backporting fixes: - Fix: DistributionLoss not encodable (#2092 by @jaheba)

- Python
Published by lostella over 3 years ago

https://github.com/awslabs/gluonts - 0.10.0 rc1

Overview

Arrow based datasets

We have added support for Parquet-files, as well as Arrow's binary format. This is an opt-in feature, requiring pyarrow to be installed. Use pip install 'gluonts[pro]' or pip install 'gluonts[arrow]' to ensure the correct version is installed.

FileDataset has been reworked to support .parquet and .arrow files. Previously, it had assumed all files to use jsonlines. To continue using jsonlines ensure that the the files use one of the .json, .jsonl, .json.gz, jsonl.gz suffixes.

Depending on the dataset size and shape, Arrow can be much faster than the json variant. In more extreme cases we saw speedups of more than 100x when using arrow vs jsonlines (see #2003 for some examples).

To convert a given dataset into arrow, you can use the gluonts.dataset.arrow utility:

sh python -m gluonts.dataset.arrow write </path/to/dataset> my-dataset.arrow`

`PandasDataset`

We have added support for pandas.DataFrame and pandas.Series as well. You can now directly model data given in a DataFrame using gluonts.dataset.pandas.PandasDataset. In this tutorial we describe in depth how you can use PandasDataset to speed up modelling using GluonTS.

Changelog

New Features

#1631 - Add TimeLimitCallback to mx/trainer callbacks. (by @yx1215)
#1780 - adding MQF2 (Multi-horizon) (by @KelvinKan)
#1903 - Added QuarterlyBegin time feature (by @kashif)
#1924 - Porting SimpleFeedForwardEstimator to PyTorch (by @lostella)
#1925 - DeepAR PyTorch: make samplers configurable (by @lostella)
#1935 - added support for pandas dataframes (by @rsnirwan)
#1962 - Add support for beta-NLL loss (by @kashif)
#1982 - Add Uber-TLC dataset to dataset repository. (by @Hongqing-work)
#1990 - Add info cli. (by @jaheba)
#1987 - Add HP tuning example with Optuna (by @npnv)
#2000 - Add arrow-based dataset. (by @vafl, @lostella, @jaheba)
#2002 - add ND for item_metrics (by @melopeo)
#2006 - Added support of "long" RTS, making short RTS be "pastfeatdynamic_real" (by @zoolhasson)
#2061 - Add DatasetWriter. (by @jaheba)
#2074 - Add support for second frequency. (by @kashif)

Breaking Changes

#1917 - Breaking: Fix return types of features (by @lostella)
#1941 - Breaking: Update dependency fbprophet -> prophet (by @lostella)
#1946 - Breaking: Split incremental quantile output into separate class (by @lostella)
#1965 - Breaking: reorg torch package, shorten import paths (by @lostella)
#1980 - Use pd.Period instead of pd.Timestamp. (by @jaheba)
#1997 - Remove freq argument from Forecast. (by @kashif)
#2011 - Remove dct_reduce. (by @jaheba)
#2018 - Remove multiprocessing dataloader. (by @jaheba)
#2019 - Rework FileDataset. (by @jaheba)
#2053 - Add dataset_writer to get_dataset. (by @Hongqing-work)
#2070 - Add jsonl.encode_json, remove serialize_data_entry. (by @jaheba)

Bug Fixes / Minor Improvements

#1704 - Settings._let will pop element it added instead of just the last one. (by @jaheba)
#1905 - Fix typing issues in torch estimators, update base estimators docstrings (by @lostella)
#1909 - Fix the use of the scaling parameter in Transformer model (by @StanislasGuinel)
#1916 - Fix AddTimeFeatures transformation for multiples of base frequencies (by @lostella)
#1920 - Fix: use broadcast_lesser in place of comparisons in ISQF (by @vincentqb)
#1931 - Fix dummy estimator (by @canerturkmen)
#1933 - Fix Pytorch Lightning tutorial. (by @jaheba)
#1938 - Fixed autograd inplace operations error in Transformed Distribution (by @shubhamkapoor)
#1950 - Fix: Hard threshold positive distribution parameters (by @lostella)
#1952 - Fix forecast keys (quantiles) output by TemporalFusionTransformer (by @lostella)
#1968 - Fix: use of numparallelsamples in deepAR (by @kashif)
#1969 - Fix: torch DeepAR observed indicator in multivariate case (by @kashif)
#1975 - use FieldName (by @kashif)
#1983 - Documentation: add docstrings for torch-based models (by @lostella)
#1986 - Fix OffsetSplitter for negative offsets (by @lostella)
#1989 - Pin protobuf version. (by @jaheba)
#1991 - Remove packaged pytorch-ts from gluonts.nursery.SCott (by @lostella)
#1999 - Documentation: fix and speed up tutorials (by @lostella)
#2004 - Refactor splitter assertion and add error message (by @RSNirwan)
#2005 - Rework itertools, add col-to-row and row-to-col functions. (by @jaheba)
#2008 - Re-add cache for parsing 'pd.Period'. (by @jaheba)
#2013 - Update website template, clean up homepage and tutorials (by @lostella)
#2014 - Expose Estimator, Predictor, Forecast in gluonts.model. (by @jaheba)
#2015 - Fix mean in AffineTransformedDistribution (by @stailx)
#2016 - Fix torch affine transformed distribution (by @lostella)
#2020 - Remove unnecessary files from docs folder, update gitignore (by @lostella)
#2021 - Update references to dev branch. (by @lostella)
#2024 - Fix README. Use DataFramesDataset. (by @jaheba)
#2025 - Make HP tuning tutorial more accurate (by @jaheba)
#2028 - Re-add support for Python 3.6 (by @jaheba)
#2029 - Add support for nan values in Rotbaum (by @zoolhasson)
#2035 - Simplify lag values computation in torch DeepAR (by @lostella)
#2036 - Minor improvements to the hierarchical model (by @rshyamsundar)
#2047 - Make Quantile derive from pydantic.BaseModel. (by @jaheba)
#2050 - Add concepts section to docs. (by @jaheba)
#2051 - Add tutorial on DataFramesDataset (by @RSNirwan)
#2057 - Add optional parameter time_axis to forecast_start. (by @melopeo)
#2062 - Fix type annotations for predict_to_numpy (by @lostella)
#2068 - Docs: simplify call to evaluator (by @lostella)

- Python
Published by jaheba over 3 years ago

https://github.com/awslabs/gluonts - 0.9.5

Re-add support for Python 3.6 in v0.9.x. (#2032 by @jaheba)

Backporting fixes:

Fix: use of num_parallel_samples in deepAR (#1968 by @kashif)
Fix: torch DeepAR observed indicator in multivariate case (#1969 by @kashif)
Fix OffsetSplitter for negative offsets (#1986 by @lostella)
Fix mean in AffineTransformedDistribution (#2015 by @stailx)

- Python
Published by jaheba over 3 years ago

https://github.com/awslabs/gluonts - 0.9.4

Backporting fixes: * Fix: Hard threshold positive distribution parameters (#1950 by @lostella) * Fix forecast keys (quantiles) output by TemporalFusionTransformer (#1952 by @lostella)

- Python
Published by lostella almost 4 years ago

https://github.com/awslabs/gluonts - 0.9.3

Backporting fixes: * Fix: use broadcast_lesser in place of comparisons in ISQF (#1920 by @vincentqb) * Fix dummy estimator (#1931 by @canerturkmen) * Fix Pytorch Lightning tutorial (#1933 by @jaheba) * Fixed autograd inplace operations error in Transformed Distribution (#1938 by @shubhamkapoor)

- Python
Published by lostella almost 4 years ago

https://github.com/awslabs/gluonts - 0.9.2

Backporting fixes: * Fix AddTimeFeatures transformation for multiples of base frequencies (https://github.com/awslabs/gluon-ts/pull/1916) * Update docs requirements (https://github.com/awslabs/gluon-ts/pull/1919)

- Python
Published by lostella almost 4 years ago

https://github.com/awslabs/gluonts - 0.9.1

Backporting fixes: * Added QuarterlyBegin time feature (#1903) * Fix the use of the scaling parameter in Transformer model (#1909)

- Python
Published by lostella almost 4 years ago

https://github.com/awslabs/gluonts - 0.9.0

Changelog

New Features

Add ckpt_path argument to PyTorchLightningEstimator. (#1872)
Add TSBench (#1865)
add SCott code to nursery (#1827)
Add dynamic code for shell. (#1821)
Adding torch.isqf (#1815)
Add tsbench readme placeholder (#1808)
Adding ISQF distribution class (#1746)
Adding IQF to remove quantile crossing and required retraining for ne… (#1693)
Hierarchical Forecaster: End-to-End model based on DeepVAR (#1665)
Adding glouonts.torch.piecewise_linear (#1663)
Add quantitle regression mode to AutoGluon-based TabularEstimator (#1611)
add dummy estimator to trivial models (#1602)

Bug Fixes

Add file path argument to m5 dataset generation (#1896)
Fix negative binomial parameter map (#1893)
Fix negative binomial sampling (#1884)
Fixes for Monash Forecasting Repository datasets (#1879)
Fix serde.flat type handling. (#1851)
Fix datesplitter. (#1850)
changed metadata creation function (#1847)
Check equality of transformations. (#1844)
Fix samples scaling in PyTorch DeepAR (#1836)
Fix _version for cases when git is not installed. (#1825)
Fixed data leakage bug in implementation of dynamic real and categorical features (#1809)
fix for #1725, reverse breaking changes to data loader and handle all zero batches (#1779)
Upgrade pytorch and pytorch-lightning requirements and some fixes. (#1765)
Fix torch NOPScaler shape. (#1752)
Convert batchify list to np array (#1732)
Fix gluonts.json; added bdump/bdumps. (#1721)
Fix scaling for pytorch negative binomial output (#1702)
Fix frequency string conversion from ts format, add test (#1652)
Fix NegativeBinomial constructor args in NegativeBinomialOutput (torch) (#1651)
Add batch_size attribute to MQCNNEstimator and MQRNNEstimator (#1645)
Add additional datasets from the Monash Time Series Forecasting Repository (#1632)

Breaking Changes

Extend default quantiles for MQ* Estimators to match MSIS quantiles. (#1866)
changed metadata creation function (#1847)
Remove support module. (#1792)
Set minimum Python version to 3.7. (#1791)
Exceptions cleanup. (#1615)

Other Changes & Improvements

Update mypy to 0.910. (#1875)
Bump ujson from 4.3.0 to 5.1.0 in /src/gluonts/nursery/tsbench (#1869)
Update black to v22. (#1867)
Fix docstring typo in feature.py (#1863)
Fix scott checks. (#1845)
Remove requirement for @validated in from_hyperparameters. (#1826)
Fix test collect ignore. (#1817)
Split tests into one workflow for each framework. (#1805)
Mark transformer as flaky. (#1801)
Mark empirical_distribution test as flaky. (#1798)
Use of int/float/object over np.int/float/object for dtype. (#1795)
Rework tests. (#1786)
Update typing_extension version. (#1785)
Use of independent random seed. (#1767)
Upgrade pytorch and pytorch-lightning requirements and some fixes. (#1765)
Remove sphinx-autobuild sphinx-autorun, update sphinx version. (#1745)
Exlude bin folders from apidoc. (#1744)
Don't run doctest on nursery. (#1743)
Hierarchical: Compute relative reconciliation error and add tests (#1722)
Fixing doc build from mqcnn-iqf commit (#1699)
Replace miniver with custom versioning code. (#1662)
Cap numba<0.54, ipykernel<6.2.0 (#1661)
Removed assert for cardinality and static feats (#1659)

- Python
Published by lostella about 4 years ago

https://github.com/awslabs/gluonts - 0.8.1

Backporting fixes: * loosen RTOL in test/distribution/test_flows.py to make test_flow_invertibility pass (#1604) * Add batch_size attribute to MQCNNEstimator and MQRNNEstimator (#1645) * Fix NegativeBinomial constructor args in NegativeBinomialOutput (torch) (#1651) * Fix frequency string conversion from ts format, add test (adapted from #1652)

- Python
Published by lostella over 4 years ago

https://github.com/awslabs/gluonts - 0.7.7

Backporting fixes: * Fix frequency metadata bug for lstnet datasets (#1593) * Add batch_size attribute to MQCNNEstimator and MQRNNEstimator (#1645) * Fix NegativeBinomial constructor args in NegativeBinomialOutput (torch) (#1651)

- Python
Published by lostella over 4 years ago

https://github.com/awslabs/gluonts - 0.8.0

New Features

add dummy estimator for seasonal_naive (#1598)
Add STL-AR as one more R baseline model (#1568)
Allow validation data for TabularEstimator. (#1562)
QRX fixes and added functionality (#1544)
Extend FileDataset's Parameters to load_datasets (#1538)
Serde: Allow encoding of functions and methods. (#1519)
Settings: Enable partial assignment (#1504)
Settings: Support for nested args in _inject. (#1503)
Transform.apply (#1494)
PyTorch implementation of DeepAR (#1460)
support Min freq for get_seasonality() method (#1459)
add deep renewal processes for intermittent demand forecasting (#1458)
Add transform objects for dealing with sparse time series. (#1421)
spliced binned pareto (#1410)
Add callbacks mechanism to Trainer class (#1168)

Bug Fixes

Fix frequency metadata bug for lstnet datasets (#1593)
Fix single dispatch register for py36 (#1591)
R fixes for methods that produce point forecasts or prediction intervals directly (#1564)
Fix computation of OWA (#1557)
Fixed QRX bug: ".values()" to ".values" (#1552)
QRX fixes and added functionality (#1544)
Fix serde issue with some distribution output types, add test (#1543)
Add item_id to r forecast predictors (#1537)
fix ProphetPredictor serialization issue (#1535)
Add constant dummy time features to TFT for yearly data (#1518)
Settings: Fix partial assignment. (#1516)
Fix anomaly detection example (#1515)
Fix Settings._inject to check if it can provide the value. (#1501)
Change miniver fallback version from unknown to 0.0.0. (#1457)
Fix getlagsfor_frequency for minute data in DeepVAR (#1455)
Fix missing import in gluonts.mx.model.GluonEstimator (#1450)
Fix train-test split data leakage for m4yearly and wiki-rollingnips. (#1445)
fix compatibility for pandas < 1.1 in time_feature/_base.py (#1437)
fix edge case in iteration based model averaging (#1345)

Breaking Changes

QRX fixes and added functionality (#1544)
Transform.apply (#1494)

Other Changes & Improvements

shallow import for gluonts.mx module (#1592)
Mark torch distribution inference tests as flaky (#1586)
Update REFERENCES.md (#1583)
Delete pytorchpredictorexample.ipynb (#1574)
Improve tests for R methods (#1567)
Rename flake8 action step. (#1555)
Set maxidletransforms to the length of the dataset (#1546)
Add datasets from forecastingdata.org (#1542)
Train invoke with (#1530)
Consolidate ZeroFeature from DeepState (#1522)
Fix indentation (#1500)
Simplify loader.py (#1495)
adjustments to variable length functionality in batchify (#1442)
Use miniver for version resolution. (#1434)
Add docstrings for metrics. (#1422)
Fixes for MXNet 1.8 (#1403)

- Python
Published by Schmedu over 4 years ago

https://github.com/awslabs/gluonts - 0.7.6

Backporting fixes: - Fix serde issue with some distribution output types, add test (#1543)

- Python
Published by Schmedu over 4 years ago

https://github.com/awslabs/gluonts - 0.7.5

Backporting fixes: - Train invoke with (#1530) - fix ProphetPredictor serialization issue (#1535) - Add item_id to r forecast predictors (#1537) - Serde: Allow encoding of functions and methods. (#1519) - Disable tests on Windows for PRs, fix other workflows (#1525)

- Python
Published by Schmedu over 4 years ago

https://github.com/awslabs/gluonts - 0.7.4

Backporting fixes: - Fix Settings._inject to check if it can provide the value. (#1501) - Fix indentation (#1500) - Fix anomaly detection example (#1515) - Add constant dummy time features to TFT for yearly data (#1518)

- Python
Published by Schmedu over 4 years ago

https://github.com/awslabs/gluonts - 0.7.3

Backporting fixes: - Fix getlagsfor_frequency for minute data in DeepVAR (#1455)

- Python
Published by Schmedu almost 5 years ago

https://github.com/awslabs/gluonts - 0.7.2

Backporting fixes: - Fixes for MXNet 1.8 (#1403) - Fix train-test split data leakage for m4yearly and wiki-rollingnips. (#1445) - Lock the version for mxnet theme to 0.3.15 (#1451) - Fix missing import in gluonts.mx.model.GluonEstimator (#1450)

- Python
Published by Schmedu almost 5 years ago

https://github.com/awslabs/gluonts - 0.6.9

Backporting fixes: - Fix train-test split data leakage for m4yearly and wiki-rollingnips. (#1445) - Lock the version for mxnet theme to 0.3.15 (#1451)

- Python
Published by Schmedu almost 5 years ago

https://github.com/awslabs/gluonts - 0.6.8

Backporting fixes: - fix s3fs ImportError for fsspec by updating the requirement depending on the python version (#1391) - fix compatibility for pandas < 1.1 in time_feature/_base.py (#1437)

- Python
Published by Schmedu almost 5 years ago

https://github.com/awslabs/gluonts - 0.7.1

Backporting fixes: - fix compatibility for pandas < 1.1 in time_feature/_base.py (#1437)

- Python
Published by Schmedu almost 5 years ago

https://github.com/awslabs/gluonts - 0.7.0

GluonTS adds improved support for PyTorch-based models, new options for existing models, and general improvements to components and tooling.

Breaking changes

This release comes with a few breaking changes (but for good reasons). In particular, models trained and serialized prior to 0.7.0 may not be de-serializable using 0.7.0.

Changes in model components and abstractions:
- #1256 and #1206 contain significant changes to the GluonEstimator abstract class, as well as InstanceSplitter and InstanceSampler implementations. You are affected by this change only if you implemented custom models based on GluonEstimator. The change makes it easier to define (and understand, in case you're reading the code) how fixed-length instances are to be sampled from the original dataset for training or validation purposes. Furthermore, this PR breaks data transformation into more explicit "pre-processing" steps (deterministic ones, e.g. feature engineering) vs "iteration" steps (possibly random, e.g. random training instance sampling), so that a cache_data option is now available in the train method to have the pre-processed data cached to memory, and be iterated quicker, whenever it fits.
- #1233 splits normalized/unnormalized time features from gluonts.time_features into distinct types.
- #1223 updates the interface of ISSM types, making it easier to define custom ones e.g. by having a custom set of seasonality patterns. Related changes to DeepStateEstimator enable these customizations when defining a DeepState model.
Changes in Trainer:
- #1178 removes the input_names argument from the __call__ method. Now the provided data loaders are expected to produce batches containing only the fields that the network being trained consumes. This can be easily obtained by transforming the dataset with SelectFields.
Package structure reorg:
- #1183 puts all MXNet-dependant modules under gluonts.mx, with some exceptions (gluonts.model and gluonts.nursery). With the new structure, one is not forced to install MXNet unless they specifically require modules that depend on it.
- #1402 makes the Evaluator class lighter, by moving the evaluation metrics to gluonts.evaluation.metrics instead of having them as static methods of the class.

New features

PyTorch support: * PyTorchPredictor serde (#1086) * Add equality operator for PytorchPredictor (#1190) * Allow Pytorch predictor to be trained and loaded on different devices (#1244) * Add distribution-based forecast types for torch, output layers, tests (#1266) * Add more distribution output classes for PyTorch, add tests (#1272) * Add pytorch tutorial notebook (#1289)

Distributions: * Zero Inflated Poisson Distribution (#1130) * GenPareto cdf and quantile functions (#1142) * Added quantile function based on cdf bisection (#1145) * Add AffineTransformedDistribution (#1161)

Models: * add estimator/predictor types for autogluon tabular (#1105) * Added thetaf method to the R predictor (#1281) * Adding neural ode code for lotka volterra and corresponding notebook (#1023) * Added lightgbm support for QRX/Rotbaum (#1365) * Deepar imputation model (#1380) * Initial commit for GMM-TPP (#1397)

Datasets & tooling: * Implemented generaterollingdatasets (#844) * Add a MinMax scaler (#1134) * introduce functional api for data generation recipes (#1153) * include m3 dataset (#1169) * Improvements for data generation (#1195) * Add most forecasters as entry points. (#1351)

- Python
Published by lostella almost 5 years ago

https://github.com/awslabs/gluonts - 0.6.7

Backporting fixes: - Added leadtime argument to PyTorchPredictor (#1316) - Fix serialization of leadtime (#1328) - Fix serialization of lead_time in torch predictor (#1329)

- Python
Published by Schmedu about 5 years ago

https://github.com/awslabs/gluonts - 0.6.6

Backporting fixes: * Use broadcastlogicalor in inflatedbeta (#1226) * Fix type error for using quantileweights and add a proper Pytest (#1231) * Fixed MASE in N-BEATS: removed redundant factor (#1288) * Fixing bug where dropout was not used, also remove unused halt option (#1315) * Fixes for Python 3.8 (#1318)

- Python
Published by Schmedu about 5 years ago

https://github.com/awslabs/gluonts - 0.6.5

Backporting fixes: * Fix serde for np.dtype. (#1299)

- Python
Published by lostella about 5 years ago

https://github.com/awslabs/gluonts - 0.6.4

Backporting fixes: - PyTorchPredictor serde (#1086) - Add equality operator for PytorchPredictor (#1190) - fix pytorch predictor serde (#1194)

- Python
Published by Schmedu about 5 years ago

https://github.com/awslabs/gluonts - 0.6.3

Backporting fixes: - fixes in dataset mutability (#1171) - Added item_id to NPTS and Naive2 forecasts (#1173)

- Python
Published by Schmedu about 5 years ago

https://github.com/awslabs/gluonts - 0.6.2

Backporting fixes:

fix chain method of Transformation (#1156)

- Python
Published by Schmedu about 5 years ago

https://github.com/awslabs/gluonts - 0.6.1

Backporting fixes: - Masking edge case fix (#1137)

- Python
Published by Schmedu over 5 years ago

https://github.com/awslabs/gluonts - 0.6.0

Changelog

New Features:

Model averaging (#823) add SampleForecast and Predictor objects for TPPs (#819) Add temperature scaling to categorical distrubution (#792) Representation module (#755) New methods for missing value imputation (#843) Add shuffling function (#873) make distributions pickleable (#889) Aggregate lag transformation (#886) SimpleFeedForward to produce DistributionForecast (#870) Added support for gzipped files. (#914) MQCNN: Support for past dynamic features and scaling (#916) Implemented model iteration averaging to reduce model variance (#901) add nan support to simple feedforward model (#933) Added timeout option for batch requests. (#931) Implemented moving average (#926) NanMixture: Distribution to model missing values (#913) Added Rotbaum (#653) DeepTPP: RNN-based temporal point processes model (#976) Added logging of scored instances for batch-transform. (#1010) Enable distribution output in seq2seq (#1008) Implemented activation regularization (#955) Adding mean absolute quantile loss to avoid the case of dividing by 0 as a possible HPO metric (#1012) Inflated Beta Distributions (#1018) Implement different dropout strategies (#963) Adding support for num_forking as MQ-CNN hp (#1022) Generalised Pareto distribution (#1031) Added use of supported quantiles in shell when QuantileForecastGenerator is used. (#1048) PyTorch Predictor (#1051) Add TFT model (#962) ConvTrans Implementation (#961) Add evaluation metrics for anomaly detection (#1065) Add piecewise linear quantile function output with fixed knots (#1074) include callback in trainer and example for warm starting (#1087) initial pytorch distribution output class (#1082) Glide (#995) specialize plot method for QuantileForecast (#1114)

Bug fixess

Fixed disabling of tqdm. (#839) Fix comparison of ParameterDict when non prefixed variables are in dict. (#859) Fixing edge case of prediction length 1. (#867) Frequency String for Pandas Timestamp (#884) fix imports (#885) Fixed invalid numworker possibility. (#892) Corrected the formula for the stddev of the MixtureDistribution. (#900) Fix pathes in R for Windows. (#903) Scale the negative binomial's gamma (#909) Shape squeeze edge case bug fix (#911) Use of \n to split lines in batch transform. (#920) Fixing cardinality array when usefeatstaticcat = False but featstaticcat present in dataset (#918) Fix batch-transform case, where request is empty. (#927) fix DeterministicOutput, add tests (#982) Fixing the FileDataset case with caching off for numworkers calculation (#986) Overriding early stopping for iteration-based averaging strategies (#993) Bug Fixes, Warnings, and One-Hot Encodings for Rotbaum (#980) Fixing case with only time features and yearly freq (#1002) Fixed import of Trainer. (#1005) Fixed DeepAR typing error (#1017) Fix sampling for MixtureDistribution class (#1042) MQ-CNN: Bound contextlength by the maxtslen - predictionlength (#1037) Fix gamma nans (#1061) Fix scaling for MQ-(C|R)NN when distribution outputs are used (#1070) added value in support to mixture output (#1077) fix Gamma distribution's NaN gradients for zero inputs (#1078) Fix dataset.splitter maxhistory argument (#1085) Fix max window (#1097) Ignore NaN values during training and throw a warning (training got stuck before) (#1104) Fix a few bugs about tensor shapes in default values for TFT implementation (#1093) Fixes awslabs/gluon-ts#1106 (#1125)

Breaking changes

Mqcnn rts (#668) Changed dataset.splitter to use DataEntry instead of TimeSeriesItem (#890) Refactoring data loading utilities (#898) Removed TimeSeriesItem. (#904) refactor imputation transformation (#907) making backtestmetrics simpler (#924) Moved getseasonality from evaluation to time_feature. (#971) Removed mxContext from core. (#977)

Other changes and improvements

Update bugreport.md (#835) Dockerfile for R container added (#841) Added mx module. (#876) Adapted use of mx module. Applied isort. (#878) Simplified AsNumpyArray. (#879) Removed unused Transformation.estimate. (#880) Added README to shell. (#882) Docs requirements (#883) add documentation related to shufflebufferlength/ (#910) Default QuantileForecast.mean to p50. (#930) Addded trimming to encoded sagemaker parameters in shell package. (#917) Shell: Fix writing of output/failure file in case of error in provided hyper-parameters. (#942) Pass listifydataset as a hyperparameter through the shell (#934) Evaluation metrics now stored in output folder (#938) Make TrainEnv path argument explicit. (#943) Removing mp worker del method. (#944) Fixed logical error in dataloder tests. (#951) Pass multiprocessing parameters through the shell (#952) Fix pandas requirement. (#967) Fix shell.train. Moved Dockerfiles to examples/dockerfiles (#946) Cleaned up unused imports. (#1007) Fix docstrings for SimpleFeedForward (#1009) Fix docstrings, enable distroutput in MQRNN (#1021) Update README.md (#1024) Update holidays version (#1033) improved and simplified aggregate lag transformation (#1028) Refactoring forecast generators and predictors for framework independence (#1052) Improved logging for batch-transform. (#1059) Reverting #1042 and adding shape assertions to the MixtureDistribution (#1058) Using padtosize function to remove duplicate code in padarrays (#1047) re-organized modules and imports (#1068) speed labelstoranges using numba (#1071) Fix numba warning; mask np.nan labels (#1072) added PyTorch predictor example notebook (#1053) refactor multiprocessing batcher to work with spawn method (#1080) Using zero floating point tolerance in denominator rather than checkign for exact zero equality (#1079) Fix FieldNames of Train/test splitter (#1083) Added Stateful to serde. (#1088) Added ty.checked decorator. (#1091) update links in readme (#1090) Adding testquantiles hyperparameter to the shell to specify the quantiles for evaluation (#1096) Refactored serde into a package. (#1100) Refactored shell. (#1101) Updated pytest to v5. (#1102) cap pydantic version (#1115) add item_id to forecast from seasonal naive (#1113) reduce number of batches used in test (#1131) fix pandas usage and remove version cap (#1132)

- Python
Published by Schmedu over 5 years ago

https://github.com/awslabs/gluonts - 0.5.2

Backporting fixes:

remove kwargs from hybrid_forward input name inference (#846)
Fix the quantiles used to compute MSIS (#849)
Fix totimeseries_item in splitter (#874)
updated Pandas deprecation (#875)
Updated Frequency String for Pandas Timestamp (#884)
splitting features and uses DataEntry instead of TimeSeriesItem (#890)
fix multiprocessing in backtest function (#915)
Fix LDS distribution for the case where sequence_length==1 (#921)
Defaulting MSIS to NaN when it can't be calculated. (#923)

- Python
Published by lostella over 5 years ago

https://github.com/awslabs/gluonts - 0.5.1

Fix pandas version to 1.0.x.

- Python
Published by jaheba over 5 years ago

https://github.com/awslabs/gluonts - 0.5.0

Changelog

New features

Dirichlet Multinomial distribution (#482)
Datasets from the GP-Copula paper (#476)
Marginal CDFtoGaussianTransformation (#486)
DeepVAR model (#491)
GP-Copula model (#497)
Add transform objects for temporal point processes (#341)
Added operator to allow for easier chaining of transformations. (#505)
Gamma distribution implemented. (#502)
Beta distribution implemented. (#512)
Sagemaker SDK Integration (#444, #585)
Add loc argument to distribution output classes (#540)
Shopping holidays (#542)
Add Poisson distribution (#532)
N-Beats model (#553, #588, #655)
Support slicing of distributions (#645)
Naive2 model and OWA evaluation metric (#602)
Add LSTNet (#596, #700, #791, #804)
Data loading utils for M5 competition datasets (#716)
Add MAPE to evaluator (#725)
Add label smoothing to binned distribution (#731)
Multiprocessing data loader. (#689, #739, #747, #759, #742)
Add Categorical Distribution (#746)
Added multiprocessing support for evaluation. (#741)
Add variable length functionality to DataLoaders (#780)
Add axis option to Scaler classes (#790)
Add lead_time to predictors and estimators (#700)
Add logit normal distribution (#811)

Bug fixes

Fix instance splitter issue with short time series (#533)
Fixed distribution sampling issues. (#526)
Fix quantile of Binned distribution (#536)
Fixed FileDataset SourceContext (#538)
Fix quantile fn for transformed distribution (#544)
Fix bug in cdf method of piecewise linear distributions (#564)
Fixed taxi dataset cardinality (#552)
Fix item_id field in provided datasets (#566)
Fix Dockerfile to use Python 3.7. (#579)
Fix DeepState trend model to work in symbolic mode (#578)
Fix for symbol block serialization issue (#582, #591)
Fixed LSTNet implementation (#586, )
Fix mean_ts method of Forecast objects (#624)
Fix r-forecast package on windows. (#626)
Fix forecast index bug, add test (#644)
Fix the sign method of affine transformation (#613)
Fixing context when converting to symbol block predictor (#651)
Fix data loader and include validation channel in test (#680)
Fix incompatible date_range and matplotlib register in pandas v1.0 (#679)
Fix binned distribution for mxnet 1.6 (#728)
Remove asserts on loc and scale (#734)
Fix default scaler in seq2seq models (#745)
Fix pydanitc create_model usage. (#768)
Fix feature slicing in WavenetSampler (#770)
Fix bug with iteration over datasets (#787)
Use forecast_start in RForecastPredictor (#798)
Fix negative binomial's scaling (#719, #814)

Breaking changes

Moved gp module to be part of gp_forecaster. (#572)

Other changes and improvements

Changed FileDataset to be more easily inheritable. (#498)
Added strategies for timezone information. (#500)
Split up transform into its own module. (#499)
Distribution dependent loss masking. (#534)
Remove dataset class in favor of alias (#560)
Clean up lifted operations, add pow operation (#571)
Removed expand_dims when reading in time-series values. (#574)
Updated dependency to Pandas v1.0 (#576)
Refactored DataLoader. (#619)
Refactored instance sampler. (#648)
Log epochs in trainer (#676)
Improve trainer handling of learning rate scheduling and logging (#701)
Upgrade to mxnet 1.6 (#709)
Moved model tests into their own folders. (#727)
Refactor wavenet model (#743)
Disable TQDM when running on SageMaker. (#810)

- Python
Published by lostella almost 6 years ago

https://github.com/awslabs/gluonts - 0.4.3

Changelog

Fix that allows GluonTS to work with the latest pydantic v1.5 (#783)

- Python
Published by lostella almost 6 years ago

https://github.com/awslabs/gluonts - 0.4.2

Fix WaveNet prediction length during training (#347)
Relax requirements constraints (#456)
Added aggregation functionality to MultivariateEvaluator (#459)
Removed unused static method in DeepARNetwork (#460)
Updated pydantic to version 1. (#465)
Fix use of numpy.histogram. (#472)
Fix validation error in transformed distribution (#475)
Refined doc requiremnents; using sphinx 2. (#477)

- Python
Published by jaheba about 6 years ago

https://github.com/awslabs/gluonts - 0.4.1

Changelog

v0.4.1 includes:

Added median as alias for p50 to Forecast. (#450)
Use validation to prevent overfitting (#378)
Fix deepstate serialization, add tests (#445)
Fix escaping of string in serde.dump_code. (#439)
Added multivariate grouper and tests (#432)
Fixes to setup.py to make it work on Windows (#433)

- Python
Published by jaheba over 6 years ago

https://github.com/awslabs/gluonts - 0.4.0

Models

Added Deep State model. (#229)
Added Deep Factor model. (#271)
Fixed bug when changing default activation function in WaveNet (#299)
Option for DeepAR and DeepState to allow an embedding vector instead of the same value for all categorical features. (#315)
Add option for featstaticreal in DeepAREstimator. (#324)
Fixed DeepState samples tensor shape. (#340)
Added support for changing dataytpe in DeepAREstimator. (#363)
Made cardinality argument compulsory in DeepStateEstimator. (#413)
DeepStateEstimator: Some adjustments to hyperparameter settings. (#415)

Distributions

Include quantile method in distribution. (#314)
Added slice_axis methods to Distribution. (#397)
Added Dirichlet distribution. (#417)

Other new features

Added more operators for synthetic data generation. (#286)
Included DistributionForecast and make plot generic. (#316)

Bug fixes

Updated lag error message. (#266)
Fix mistake in notebook. (#269)
Fix pandas warnings in dataset generation. (#270)
Fix numerical issue with negative binomial distribution. (#288)
Fixes fieldname issues. (#292)
Fixed a wrong reshaping in DeepAR estimator. (#330)
Small fixes to Box-Cox transformation. (#349)
Improve BinnedDistribution. (#350)
Small fix for binned distribution. (#352)
Assure Learning Rate Scheduler does not increase the learning rate. (#359)
Fix dim and copy_dim methods in SampleForecast. (#366)
Fixed the logging of the number of parameters during training. (#386)
Fix empty time_features issue. (#387)
Fix batch shape in Binned Distribution (#406)
Fix bug in multivariate Gaussian. (#407)
Fix edge case in evaluation where prediction length is 1 and prediction target is nan. (#422)

Other changes

Make item_id field uniform across predictors. (#268)
Added Dockerfile. (#285)
Pytest-timeout==1.3; removes warnings from logs. (#306)
Flask~=1.1; removes some warnings. (#307)
Make tensors and distributions serializable. (#312)
Added SageMaker batch transform support. (#317)
Manage mxnet context when deserializing predictors. (#318)
Add missing time features for business day frequency. (#325)
Switched to timestamp alignment from rollback to rollforward. (#328)
Adding GPU support to the cholesky jitter and eig tests. (#342)
Adding GP example on synthetic dataset with built-in plotting. (#343)
Introduced ForecastGenerator to wrap mxnet output into forecast object. (#348)
Add synthetic data generation tutorial. (#356)
Added pd.Timestamp to serde. (#357)
Using custom SerDe methods for deserializing params in Sagemaker. (#364)
Fixes for serializing sets and numpy numbers in SerDe. (#368)
Store GluonTS Version with stored model (#388)
Dockerfile for GPU container. Fix for installing GPU version of MXNet. (#403)
Added debug option to batch-transform. (#404)
Use static categorical feature in benchmark_m4. (#410)
Remove dataset.validate. (#412)
Renamed numevalsamples to num_samples. (#421)
Remove mxnet requirement. (#429)

- Python
Published by jaheba over 6 years ago

https://github.com/awslabs/gluonts - 0.3.3

Adapted mean predictor to use random samples. (#239)
Added predict_item to RepresentablePredictor and adapted subclasses. (#240)
Added fallback predictor and decorator.
Forecasts always start at the end of the whole target.
Fix shell to have a canonical freq key in hyperparameters.
Made fallback process-safe. Added ConstantValuePredictor.
GluonTSException bypass fallback.
Black everything. (#244)
Adding failure information to failure file. (#247)
Added error message to top of failure file. (#248)
```
fix the empty item list (#249)
```

fix the shape error of the canonical network (#251)

Fix documentation and enforce stricter doc builds (#226)

Reformatted math equations for the log_prob method of the GaussianProcess class (#252)

Fix yearly freq in process start field. (#253)

fix issue with MultivariateGaussianOutput (#257)

Fix shapes in CanonicalNetworkBase (#254)

Improvements for wavenet and some utils (#262)

```
Removed `get_granularity`. (#265)
```

- Python
Published by jaheba over 6 years ago

https://github.com/awslabs/gluonts - 0.3.2

Bump pandas version and remove timestamp workarounds (#230)
Fix numevalsamples (#232)
Fixed backtest test. (#235)
Moved simple predictors to a distinct model folder. (#237)
fix #234: Added method to fixup non json-spec compliant floats to make the resp… (#236)

- Python
Published by jaheba over 6 years ago

Recent Releases of https://github.com/awslabs/gluonts

https://github.com/awslabs/gluonts - 0.16.2

What's Changed

https://github.com/awslabs/gluonts - v0.16.1

What's Changed

https://github.com/awslabs/gluonts - 0.16.0

New features and enhancements

Fixes

Documentation

Dependencies updates

Testing

New Contributors

https://github.com/awslabs/gluonts - 0.16.0rc1

https://github.com/awslabs/gluonts - 0.15.1

https://github.com/awslabs/gluonts - 0.15.0

New features

Model updates

Breaking changes

Fixes

Dependencies

Other changes

https://github.com/awslabs/gluonts - 0.14.4

https://github.com/awslabs/gluonts - 0.14.3

https://github.com/awslabs/gluonts - 0.13.9

https://github.com/awslabs/gluonts - 0.14.2

https://github.com/awslabs/gluonts - 0.14.1

https://github.com/awslabs/gluonts - 0.13.8

https://github.com/awslabs/gluonts - 0.14.0

https://github.com/awslabs/gluonts - 0.14.0 rc2

https://github.com/awslabs/gluonts - 0.13.7

https://github.com/awslabs/gluonts - 0.14.0 rc1

https://github.com/awslabs/gluonts - 0.13.6

https://github.com/awslabs/gluonts - 0.13.5

https://github.com/awslabs/gluonts - 0.13.4

https://github.com/awslabs/gluonts - 0.13.3

https://github.com/awslabs/gluonts - 0.13.2

https://github.com/awslabs/gluonts - 0.13.1

https://github.com/awslabs/gluonts - 0.13.0

Overview

Changelog

Breaking changes

Major improvements or new features

Minor improvements or new features

Bug fixes

Documentation

Test / setup changes

Others

https://github.com/awslabs/gluonts - 0.13.0 rc1

https://github.com/awslabs/gluonts - 0.12.8

https://github.com/awslabs/gluonts - 0.12.7

https://github.com/awslabs/gluonts - 0.11.12

https://github.com/awslabs/gluonts - 0.12.6

https://github.com/awslabs/gluonts - 0.12.5

https://github.com/awslabs/gluonts - 0.12.4

https://github.com/awslabs/gluonts - 0.12.3

https://github.com/awslabs/gluonts - 0.11.11

https://github.com/awslabs/gluonts - 0.12.2

https://github.com/awslabs/gluonts - 0.11.10

https://github.com/awslabs/gluonts - 0.10.10

https://github.com/awslabs/gluonts - 0.9.10

https://github.com/awslabs/gluonts - 0.12.1

https://github.com/awslabs/gluonts - 0.10.9

What's Changed

https://github.com/awslabs/gluonts - 0.12.0

Overview

Changelog

Breaking changes

Major improvements / new features

Minor improvements / new features

Bug fixes

Documentation

Test / setup changes

Other

https://github.com/awslabs/gluonts - 0.11.9

https://github.com/awslabs/gluonts - 0.12.0 rc1

https://github.com/awslabs/gluonts - 0.11.8

https://github.com/awslabs/gluonts - 0.11.7

https://github.com/awslabs/gluonts - 0.11.6

https://github.com/awslabs/gluonts - 0.11.5

What's Changed

`PandasDataset`

`PandasDataset`