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Recent Releases of https://github.com/nixtla/neuralforecast

https://github.com/nixtla/neuralforecast - v3.0.2

Enhancements

  • Distributional predictions in predict_insample() @janovergoor (#1309)
  • Optimizations in tsdataset: reduce allocations for large datasets @tylernisonoff (#1335) ## Fixes
  • [FIX]: Add logic to load custom models when using ReduceLROnPlateau @marcopeix (#1340)
  • [FIX]: Fixes incorrect cuts in conformal prediction with conformal_error @elephaint (#1331)

- Python
Published by github-actions[bot] 12 months ago

https://github.com/nixtla/neuralforecast - v3.0.1

Features

  • FEAT: Select basis functions in NBEATS @tblume1992 @marcopeix (#1191)
  • FEAT: Add flash-attention @LeonEthan (#1295)
  • FEAT: HuberIQLoss @elephaint (#1307)

Bug Fixes

  • FIX: Fix iPython version @elephaint (#1282)
  • FIX: Recurrent predictions @elephaint (#1285)
  • FIX: Fix poor performance with the NegativeBinomial DistributionLoss @JQGoh (#1289)
  • FIX: Add excludeinsampley param to TimeXer for model loading @marcopeix (#1306)
  • FIX: Set 2.0.0<=pytorch<=2.6.0 to avoid conflicts with networkx with Python 3.9 @marcopeix (#1318)
  • FIX: Create windows once @elephaint (#1325)
  • FIX: Add htrain to RNNs & fix issue with inputsize @elephaint (#1326)
  • FIX: Allow static vars only with NBEATSx and exogenous block @marcopeix (#1319)

- Python
Published by github-actions[bot] about 1 year ago

https://github.com/nixtla/neuralforecast - v3.0.0

New features

  • FEAT: TimeXer @marcopeix (#1267)
  • All losses compatible with all types of models (e.g. univariate/multivariate, direct/recurrent) OR appropriate protection added.
  • DistributionLoss now supports the use of quantiles in predict, allowing for easy quantile retrieval for all DistributionLosses.
  • Mixture losses (GMM, PMM and NBMM) now support learned weights for weighted mixture distribution outputs.
  • Mixture losses now support the use of quantiles in predict, allowing for easy quantile retrieval.
  • Improved stability of ISQF by adding softplus protection around some parameters instead of using .abs.
  • Unified API for any quantile or any confidence level during predict for both point and distribution losses.

Enhancements

  • [DOCS] Docstrings @elephaint (#1279)
  • FIX: Minor bug fix in TFT and a nicer error message for fitting with the wrong val_size @marcopeix (#1275)
  • [FIX] Adds bfloat16 support @elephaint (#1265)
  • Recurrent models can now produce forecasts recursively or directly.
  • IQLoss now gives monotonic quantiles
  • MASE loss now works

Breaking Changes

  • [FIX] Unify API @elephaint (#1023)
  • RMoK uses the revin_affine parameter instead of revine_affine. This was a typo in the previous version.
  • All models now inherit the BaseModel class. This changes how we implement new models in neuralforecast.
  • Recurrent models now require an input_size parameter.
  • TCN and DRNN are now window models, not recurrent models
  • We cannot load a recurrent model from a previous version to v3.0.0

Bug Fixes

  • [FIX] Multivariate models give error when predicting when nseries > batchsize @elephaint (#1276)
  • [FIX]: Insample predictions with series of varying lengths @marcopeix (#1246)

Documentation

  • [DOCS] Update documentation @elephaint (#1274)
  • [DOCS] Add example of modifying the default configure_optimizers() behavior (use of ReduceLROnPlateau scheduler) @JQGoh (#1015)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/neuralforecast - v2.0.1

Enhancements

  • FEAT: Custom RNN layers for TFT @Yanam24 (#1230)
  • FEAT: Add the horizon weighing to the distribution losses @mwamsojo (#1233)

Documentation

  • DOCS: Add citation note @elephaint (#1244)
  • fix: azul @AzulGarza (#1245)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/neuralforecast - v2.0.0

Breaking Change

  • breaking: remove deprecated behavior @jmoralez (#1220)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/neuralforecast - v1.7.7

Bug Fixes

  • [FIX] Backward compatibility: missing prediction_intervals @JQGoh (#1224)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/neuralforecast - v1.7.6

New Features

  • [FEAT]: Support providing DataLoader arguments to optimize GPU usage @jasminerienecker (#1186)
  • [FEAT]: Set activation function in GRN of TFT @marcopeix (#1175)
  • [FEAT]: Conformal Predictions in NeuralForecast @JQGoh (#1171)

Bug Fixes

  • [FIX]: Ability load models saved using versions before 1.7 @tylernisonoff (#1207)
  • [FIX]: Conformal prediction issues @elephaint (#1179)
  • [FIX]: Feature importance when using only hist_exog in TFT fails @elephaint (#1174)
  • [FIX]: Remove unused output layer NBEATSx @elephaint (#1168)
  • [FIX]: Fix Tweedie loss @elephaint (#1164)
  • [FIX]: MLPMultivariate incorrect static_exog parsing @elephaint (#1170)
  • [FIX]: Deprecate activation functions for GRU @marcopeix (#1198)

Documentation

  • [DOC]: Tutorial on cross-validation @marcopeix (#1176)
  • [DOC]: Build docs on release only @elephaint (#1183)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/neuralforecast - v1.7.5

New Features

  • [FEAT]: Move RevIN class to common module @JQGoh (#1083)
  • [FEAT]: Add RMoK @marcopeix (#1148)
  • FEAT: TimeLLM is faster and supports more LLMs @ive2go (#1139)
  • [FEAT]: TFT-Interpretability @amirouyanis (#1104)
  • [FEAT]: ]Add support for the local file dataloader with the Automodels @jasminerienecker (#1095)

Bug Fixes

  • [FIX] CV Refit works with non-standard column names @elephaint (#1149)
  • [FIX]: replace self.pred_len with self.h @carusyte (#1129)
  • [FIX]: only define static encoder when applicable in TFT @jmoralez (#1114)
  • [FIX]: remove cast to float in scalers @jmoralez (#1115)
  • [FIX]: timemixer shapes mismatch and doc update @carusyte @marcopeix (#1138)

Dependencies

  • Bump pypa/gh-action-pypi-publish from 1.10.0 to 1.10.1 in the ci-dependencies group @dependabot (#1146)
  • Bump the ci-dependencies group with 2 updates @dependabot (#1135)

- Python
Published by github-actions[bot] over 1 year ago

https://github.com/nixtla/neuralforecast - v1.7.4

New Features

  • [FEAT] - Add KAN @marcopeix (#999)
  • [FEAT] - Add TimeMixer @marcopeix (#1071)
  • [FEAT] - Add support for datasets that can't fit in memory @jasminerienecker (#1049)

Bug Fixes

  • [FIX] ignore pytorch lightning's PossibleUserWarning @fabianbergermann (#1081)
  • [FIX] bug in the NBEATSx exogenous basis stack @jasminerienecker (#1072)
  • [FIX] Fix nbdev_version in test & environment @elephaint (#1089)

Documentation

  • [DOCS] add tutorial for large dataset DataLoader @jasminerienecker (#1074)
  • [DOCS] Restructure documentation @elephaint (#1063)
  • [DOCS] Fix examples @elephaint (#1092)
  • [DOCS] Fix tables @elephaint (#1090)
  • [DOCS] Fix docs layout issues @elephaint (#1085)
  • [DOCS] Fix issues @elephaint (#1082)

Dependencies

  • Bump actions/setup-python from 5.1.0 to 5.1.1 in the ci-dependencies group @dependabot (#1067)
  • use commit hash in actions and add dependabot updates @jmoralez (#1066)

- Python
Published by github-actions[bot] almost 2 years ago

https://github.com/nixtla/neuralforecast - v1.7.3

New Features

  • [FEAT] ISQF @elephaint (#1019)
  • [FEAT] - Add SOFTS model @marcopeix (#1024)
  • [FEAT] Add option to support user defined learning rate scheduler for NeuralForecast Models @JQGoh (#998)
  • [FEAT] Implicit Quantile Networks @elephaint (#1007)

Bug Fixes

  • use assign argument if available in nn.Module.loadstatedict @jmoralez (#1032)
  • update min_size in TimeSeriesDataset.append @jmoralez (#1033)
  • fix num_tasks in spark integration @jmoralez (#1028)

Documentation

  • fix: add tsmixer tutorial to sidebar @AzulGarza (#978)
  • Update models in the README @candalfigomoro (#946)

Enhancement

  • suppress warning when saving hyperparameters in base auto @jmoralez (#1034)
  • automatically set refitwithval when early stopping is enabled @jmoralez (#1031)

- Python
Published by github-actions[bot] almost 2 years ago

https://github.com/nixtla/neuralforecast - v1.7.2

New Features

  • [FEAT] DeepNPTS model @elephaint (#990)
  • [FEAT] TiDE model @elephaint (#971)

Bug Fixes

  • [FIX] Refit after validation boolean @elephaint (#991)
  • fix cross_validation results with uneven windows @jmoralez (#989)
  • [FIX] fix wrong import doc PatchTST @elephaint (#967)
  • [FIX] raise exception nbeats h=1 with stacks @elephaint (#966)

Enhancement

  • reduce default warnings @jmoralez (#974)
  • Create CODEOFCONDUCT.md @tracykteal (#972)

- Python
Published by github-actions[bot] about 2 years ago

https://github.com/nixtla/neuralforecast - v1.7.1

New Features

  • multi-node distributed training with spark @jmoralez (#935)
  • [FEAT] Add BiTCN model @elephaint (#958)
  • [FEAT] - Add iTransformer to neuralforecast @marcopeix (#944)
  • [FEAT] Add MLPMultivariate model @elephaint (#938)

Bug Fixes

  • [FIX] Fixes default settings of BiTCN @elephaint (#961)
  • [FIX] HINT not producing coherent forecasts @elephaint (#964)
  • [FIX] Fixes 948 multivariate predict/val issues when n_series > 1024 @elephaint (#962)
  • handle exogenous variables of TFT in parent class @jmoralez (#959)
  • fix early stopping in ray auto models @jmoralez (#953)
  • fix cross_validation when the id is the index @jmoralez (#951)

Documentation

  • add MLflow logging example @cargecla1 (#892)

- Python
Published by github-actions[bot] about 2 years ago

https://github.com/nixtla/neuralforecast - v1.7.0

New Features

  • [FEAT] Added TSMixerx model @elephaint (#921)
  • Add Time-LLM @marcopeix (#908)
  • [FEAT] Added TSMixer model @elephaint (#914)
  • Add option to support user defined optimizer for NeuralForecast Models @JQGoh (#901)
  • [FEAT] Added NLinear model @ggattoni (#900)
  • [FEAT] Added DLinear model @cchallu (#875)
  • support refit in cross_validation @jmoralez (#842)
  • use environment variable to get id as column in outputs @jmoralez (#841)
  • support different column names for ids, times and targets @jmoralez (#838)
  • polars support @jmoralez (#829)
  • add callbacks to auto models @jmoralez (#795)

Bug Fixes

  • [FIX] Avoid raised error for varied stepsize parameter during predictinsample() @JQGoh (#933)
  • [FIX] 926 auto ensure all models support alias and 924 Configuring hyperparameter space for Auto* Models @elephaint (#927)
  • fix base_multivariate window generation @jmoralez (#907)
  • Fix optuna multigpu @jmoralez (#889)
  • support saving and loading models with alias @jmoralez (#867)
  • [FIX] Polars .columns produces list rather than Pandas Index @akmalsoliev (#862)
  • add missing models to filename dict @jmoralez (#856)
  • ensure exogenous features are lists @jmoralez (#851)
  • fix save with save_dataset=False @jmoralez (#850)
  • copy config in optuna @jmoralez (#844)
  • Fixed: Exception: maxepochs is deprecated, use maxsteps instead. @twobitunicorn (#835)
  • fix single column 2d array polars df @jmoralez (#830)
  • move scalers to core @jmoralez (#813)
  • [FIX] Default AutoPatchTST config @cchallu (#811)
  • [FIX] ReVin Numerical Stability @dluuo (#781)
  • On Windows, prevent long trial directory names @tg2k (#735)

Documentation

  • removed documentation for missing argument @yarnabrina (#913)
  • feat: Added cross-validation tutorial @MMenchero (#897)
  • chore: update license to apache-2 @AzulGarza (#882)
  • [FEAT] Model table in README @cchallu (#880)
  • redirect to mintlify docs @jmoralez (#816)
  • add missing models to documentation @jmoralez (#775)

Dependencies

  • add windows to CI @jmoralez (#814)
  • address future warnings @jmoralez (#898)
  • use scalers from coreforecast @jmoralez (#873)
  • add python 3.11 to CI @jmoralez (#839)

Enhancement

  • Reduce device transfers @elephaint (#923)
  • extract common methods to BaseModel @jmoralez (#915)
  • remove TQDMProgressBar callback @jmoralez (#899)
  • use fsspec in save and load methods @jmoralez (#895)
  • Feature/Check input for NaNs when available_mask = 1 @JQGoh (#894)
  • switch flake8 to ruff @Borda (#871)
  • use future instead of deprecation warnings @jmoralez (#849)
  • add frequency validation and futr_df debugging methods @jmoralez (#833)

- Python
Published by release-drafter[bot] about 2 years ago

https://github.com/nixtla/neuralforecast - v1.6.4

New Features

  • TemporalNorm with ReVIN learnable parameters @kdgutier (#768)
  • support optuna in auto models @jmoralez (#763)
  • [FEAT] TimesNet model @cchallu (#757)
  • add localscalertype @jmoralez (#754)
  • [FEAT] Implementation of Exogenous - NBEATSx @akmalsoliev (#738)

Bug Fixes

  • [FIX] futrexoglist in Auto and HINT classes @cchallu (#773)
  • fix off by one error in BaseRecurrent available_ts @KeAWang (#759)

Documentation

  • [DOCS] Scaling tutorial @cchallu (#770)
  • [DOCS] Auto hyperparameter selection with optuna @cchallu (#767)
  • [DOCS] Update tutorials to v.1.6.3 @cchallu (#741)

Enhancement

  • check futrexoglist are in futr_df @jmoralez (#769)

- Python
Published by release-drafter[bot] over 2 years ago

https://github.com/nixtla/neuralforecast - v1.6.2

What's Changed

  • [FEAT] Add horizon_weight parameter to losses and BasePointLoss in https://github.com/Nixtla/neuralforecast/pull/704
  • [FIX] Fix device error in horizon_weight in https://github.com/Nixtla/neuralforecast/pull/706
  • [FIX] Base Windows padding in https://github.com/Nixtla/neuralforecast/pull/715
  • [FIX] Fixed bug in validation loss scale in https://github.com/Nixtla/neuralforecast/pull/720
  • [FIX] Base recurrent valid loss on original scale in https://github.com/Nixtla/neuralforecast/pull/721

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v1.6.1...v1.6.2

- Python
Published by FedericoGarza almost 3 years ago

https://github.com/nixtla/neuralforecast - v1.6.1

New Models

  • DeepAR
  • FEDformer

New features

  • Available Mask to specify missing data in input data frame.
  • Improve fit and cross_validation methods with use_init_models parameter to restore models to initial parameters.
  • Added robust losses: HuberLoss, TukeyLoss, HuberQLoss, and HuberMQLoss.
  • Added Bernoulli DistributionLoss to build temporal classifiers.
  • New exclude_insample_y parameter to all models to build models only based on exogenous regressors.
  • Added dropout to NBEATSx and NHITS models.
  • Improved predict method of windows-based models to create batches to control memory usage. Can be controlled with the new inference_windows_batch_size parameter.
  • Improvements to the HINT family of hierarchical models: identity reconciliation, AutoHINT, and reconciliation methods in hyperparameter selection.
  • Added inference_input_sizehyperparameter to recurrent-based methods to control historic length during inference to better control memory usage and inference times.

New tutorials and documentation

  • Neuralforecast map and How-to add new models
  • Transformers for time-series
  • Predict insample tutorial
  • Interpretable Decomposition
  • Outlier Robust Forecasting
  • Temporal Classification
  • Predictive Maintenance
  • Statistical, Machine Learning, and Neural Forecasting methods

Fixed bugs and new protections

  • Fixed bug on MinMax scalers that returned NaN values when the mask had 0 values.
  • Fixed bug on y_loc and y_scale being in different devices.
  • Added early_stopping_steps to the HINT method.
  • Added protection in the fit method of all models to stop training when training or validation loss becomes NaN. Print input and output tensors for debugging.
  • Added protection to prevent the case val_check_step > max_steps from causing an error when early stopping is enabled.
  • Added PatchTST to save and load methods dictionaries.
  • Added AutoNBEATSx to core's MODEL_DICT.
  • Added protection to the NBEATSx-i model where horizon=1 causes an error due to collapsing trend and seasonality basis.

- Python
Published by release-drafter[bot] almost 3 years ago

https://github.com/nixtla/neuralforecast - v1.5.0

What's Changed

Features

New models

  • [FEAT] VanillaTransformer, Autoformer in https://github.com/Nixtla/neuralforecast/pull/469
  • [FEAT] StemGNN in https://github.com/Nixtla/neuralforecast/pull/456
  • [FEAT] PatchTST in https://github.com/Nixtla/neuralforecast/pull/485
  • [FEAT] Informer, augmentcalendardf, set seeds in fit and predict in https://github.com/Nixtla/neuralforecast/pull/463
  • [FEAT] Hierarchical Forecasting Networks (HINT) in https://github.com/Nixtla/neuralforecast/pull/489

Misc

  • [FEAT] Added MSSE class to losses.pytorch notebook in https://github.com/Nixtla/neuralforecast/pull/468
  • [FEAT] Robustified Distribution Outputs in https://github.com/Nixtla/neuralforecast/pull/492
  • [FEAT] Added MS availability to augmentcalendardf function in https://github.com/Nixtla/neuralforecast/pull/506
  • [FEAT] Add alias argument in https://github.com/Nixtla/neuralforecast/pull/502
  • [FEAT] mean default distribution output in addition to quantiles in https://github.com/Nixtla/neuralforecast/pull/529
  • [FEAT] Predict insample in https://github.com/Nixtla/neuralforecast/pull/528

Fixes

  • [FIX] Remove fixed lib versions in https://github.com/Nixtla/neuralforecast/pull/446
  • [FIX] Fixed sCRPS in losses.pytorch notebook in https://github.com/Nixtla/neuralforecast/pull/462
  • [FIX] Compute validation loss per epoch in https://github.com/Nixtla/neuralforecast/pull/507
  • [FIX] MLP/Recurrent-based memory inference complications in https://github.com/Nixtla/neuralforecast/pull/512
  • [FIX] Fix error with inferenceinputsize in https://github.com/Nixtla/neuralforecast/pull/536
  • [FIX] Add instructions python version in https://github.com/Nixtla/neuralforecast/pull/539
  • [FIX] Predict dates bug in https://github.com/Nixtla/neuralforecast/pull/540
  • [FIX] Autoformer in https://github.com/Nixtla/neuralforecast/pull/523
  • [FIX] Removed duplicate from model collection list in https://github.com/Nixtla/neuralforecast/pull/517

Tutorials and Docs

  • [FEAT] Electricity Peak Detection in https://github.com/Nixtla/neuralforecast/pull/450
  • [FEAT] Add End to End Walkthrough tutorial in https://github.com/Nixtla/neuralforecast/pull/472
  • [DOCS] Improved HINT documentation, and broken links in https://github.com/Nixtla/neuralforecast/pull/490
  • [DOCS] HINT documentation in https://github.com/Nixtla/neuralforecast/pull/491
  • [DOCS] HINT: Updated Unit Test and Example Notebooks in https://github.com/Nixtla/neuralforecast/pull/516
  • [FEAT] HINT Unit Test in https://github.com/Nixtla/neuralforecast/pull/499

New dependencies

  • [FEAT] Add support for lightning>=2.0.0, and torch>=2.0.0 in https://github.com/Nixtla/neuralforecast/pull/498
  • [FEAT] Allow pandas 2 in https://github.com/Nixtla/neuralforecast/pull/508

New Contributors

  • @VinishUchiha made their first contribution in https://github.com/Nixtla/neuralforecast/pull/517

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v1.4.0...v1.5.0

- Python
Published by FedericoGarza about 3 years ago

https://github.com/nixtla/neuralforecast - v1.4.0

New Models

  • Temporal Convolution Network (TCN)
  • AutoNBEATSx
  • AutoTFT (Transformers)

New features

  • Recurrent models (RNN, LSTM, GRU, DilatedRNN) can now take static, historical, and future exogenous variables. These variables are combined with lags to produce "context" vectors based on MLP decoders, based on the MQ-RNN model (https://arxiv.org/pdf/1711.11053.pdf).

  • The new DistributionLoss class allows for producing probabilistic forecasts with all available models. By changing the loss hyperparameter to one of these losses, the model will learn and output the distribution parameters:

    • Bernoulli, Poisson, Normal, StudentT, Negative Binomial, and Tweedie distributions
    • Scale-decoupled optimization using Temporal Scalers to improve convergence and performance.
    • The predict method can return samples, quantiles, or distribution parameters.

  • sCRPS loss in PyTorch to minimize errors generating prediction intervals.

Optimization improvements

We included new optimization features commonly used to train neural models: * Added learning rate scheduler, using torch.optim.lr_scheduler.StepLR scheduler. The new num_lr_decays hyperparameter controls the number of decays (evenly distributed) during training. * Added Early stopping using validation loss. The new early_stop_patience_steps controls the number of validation steps with no improvement after which training will be stopped. * New validation loss hyperparameter to allow different train and validation losses

Training, scheduler, validation loss computation, and early stopping are now defined in steps (instead of epochs) to control the training procedure better. Use max_steps to define the number of training iterations. Note: max_epochs will be deprecated in the future.

New tutorials and documentation

  • Probabilistic Long-horizon forecasting
  • Save and Load Models to use them in different datasets
  • Temporal Fusion Transformer
  • Exogenous variables
  • Automatic hyperparameter tuning
  • Intermittent or Sparse Time Series
  • Detect Demand Peaks

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/neuralforecast - v1.3.0

What's Changed

  • [DOCS] Probabilistic Long-horizon forecasting in https://github.com/Nixtla/neuralforecast/pull/361
  • [FEAT]: Updated GMM Class in losses.pytorch in https://github.com/Nixtla/neuralforecast/pull/365
  • [FEAT] Scale decoupling changes for GMM and PMM class in https://github.com/Nixtla/neuralforecast/pull/366
  • [FEAT] AutoTFT in https://github.com/Nixtla/neuralforecast/pull/367
  • [FIX] Losses in Auto models initialization in https://github.com/Nixtla/neuralforecast/pull/369

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v1.2.0...v1.3.0

- Python
Published by cchallu over 3 years ago

https://github.com/nixtla/neuralforecast - v1.2.0

What's Changed

  • [FIX] Colab link getting started in https://github.com/Nixtla/neuralforecast/pull/329
  • Improved MQ-NBEATS [B,H]+[B,H,Q] -> [B,H,Q] in https://github.com/Nixtla/neuralforecast/pull/330
  • Improved MQ-NBEATSx [B,H]+[B,H,Q] -> [B,H,Q] in https://github.com/Nixtla/neuralforecast/pull/331
  • fixed pytorch losses' init documentation in https://github.com/Nixtla/neuralforecast/pull/333
  • TCN in https://github.com/Nixtla/neuralforecast/pull/332
  • Update README.md in https://github.com/Nixtla/neuralforecast/pull/335
  • [FEAT] DistributionLoss in https://github.com/Nixtla/neuralforecast/pull/339
  • [FEAT] Deprecated GMMTFT in favor of DistributionLoss' modularity in https://github.com/Nixtla/neuralforecast/pull/342
  • [Feat] Scaled Distributions in https://github.com/Nixtla/neuralforecast/pull/345
  • Deprecate AffineTransformed class in https://github.com/Nixtla/neuralforecast/pull/350
  • [FEAT] Add cla action in https://github.com/Nixtla/neuralforecast/pull/349
  • [FIX] Delete cla.yml in https://github.com/Nixtla/neuralforecast/pull/353
  • [FIX] CI tests in https://github.com/Nixtla/neuralforecast/pull/357
  • [FEAT] Added return_params to Distributions in https://github.com/Nixtla/neuralforecast/pull/348
  • [FEAT] Ignore jupyter notebooks as part of languages in https://github.com/Nixtla/neuralforecast/pull/355
  • [FEAT] Added num_samples to Distribution's initialization in https://github.com/Nixtla/neuralforecast/pull/359

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v1.1.0...v1.2.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/neuralforecast - v1.1.0

What's Changed

  • [FIX] Update license in https://github.com/Nixtla/neuralforecast/pull/285
  • [FEAT] Exogenous variables in https://github.com/Nixtla/neuralforecast/pull/286
  • scalers class in https://github.com/Nixtla/neuralforecast/pull/288
  • General documentation improvements in https://github.com/Nixtla/neuralforecast/pull/287
  • Fixed README links and added SoTA runs to examples in https://github.com/Nixtla/neuralforecast/pull/291
  • Improved documentation in https://github.com/Nixtla/neuralforecast/pull/293
  • Improved documentation in https://github.com/Nixtla/neuralforecast/pull/297
  • Improved RNN-based/BaseRecurrent/Windows in https://github.com/Nixtla/neuralforecast/pull/298
  • Save load in https://github.com/Nixtla/neuralforecast/pull/292
  • Fix normalizers in https://github.com/Nixtla/neuralforecast/pull/301
  • Improved example notebooks, changed numeration in https://github.com/Nixtla/neuralforecast/pull/302
  • Static variables in https://github.com/Nixtla/neuralforecast/pull/305
  • Turned Pytorch losses into torch.nn.module classes in https://github.com/Nixtla/neuralforecast/pull/311
  • Changed enable_checkpointing to False default in https://github.com/Nixtla/neuralforecast/pull/309
  • Correct main link pointers in https://github.com/Nixtla/neuralforecast/pull/314
  • Rnns normalizers in https://github.com/Nixtla/neuralforecast/pull/316
  • rnns with decoders, autos, usage examples, fix val in https://github.com/Nixtla/neuralforecast/pull/320
  • Improved documentation3 in https://github.com/Nixtla/neuralforecast/pull/322
  • getting started with LSTM and NHITS in https://github.com/Nixtla/neuralforecast/pull/323
  • recovered MQ-NHITS in https://github.com/Nixtla/neuralforecast/pull/327

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v1.0.0...v1.1.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/neuralforecast - v1.0.0

What's Changed

  • [BREAKING CHANGE] NeuralForecast Refactor https://github.com/Nixtla/neuralforecast/pull/281
  • [FIX] Nbdev docs https://github.com/Nixtla/neuralforecast/pull/282
  • [FEAT] Add examples in https://github.com/Nixtla/neuralforecast/pull/283

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v0.1.0...v1.0.0

- Python
Published by FedericoGarza over 3 years ago

https://github.com/nixtla/neuralforecast - v0.1.0

What's Changed

  • Added Loss Function & Rewrote Unit Testing by @shibzhou in https://github.com/Nixtla/neuralforecast/pull/238
  • fix reshapes and rnn by @cchallu in https://github.com/Nixtla/neuralforecast/pull/247
  • mqnhits by @cchallu in https://github.com/Nixtla/neuralforecast/pull/248
  • y to device by @cchallu in https://github.com/Nixtla/neuralforecast/pull/249
  • Update LICENSE to MIT by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/251

New Contributors

  • @shibzhou made their first contribution in https://github.com/Nixtla/neuralforecast/pull/238

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v0.0.9...v0.1.0

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/neuralforecast - v0.0.9

What's Changed

  • Added unit tests for numpy and pytorch losses by @kdgutier in https://github.com/Nixtla/neuralforecast/pull/232
  • fix/api auto by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/234

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v0.0.8...v0.0.9

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/neuralforecast - v0.0.8

What's Changed

  • Readme with colab by @mergenthaler in https://github.com/Nixtla/neuralforecast/pull/204
  • Utils debug by @kdgutier in https://github.com/Nixtla/neuralforecast/pull/202
  • fix scalers assert by @cchallu in https://github.com/Nixtla/neuralforecast/pull/207
  • Old auto nhits by @kdgutier in https://github.com/Nixtla/neuralforecast/pull/209
  • Fixed equality of masked mqloss and MQLoss by @kdgutier in https://github.com/Nixtla/neuralforecast/pull/215
  • TourismL hierarchical dataset by @kdgutier in https://github.com/Nixtla/neuralforecast/pull/216
  • feat: add workflow for pip by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/218
  • build(deps): bump nokogiri from 1.12.5 to 1.13.4 in /docs by @dependabot in https://github.com/Nixtla/neuralforecast/pull/219
  • fix: remove unused gem files by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/224
  • autonf class by @cchallu in https://github.com/Nixtla/neuralforecast/pull/225
  • fix: remove legacy module by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/223
  • fix: update conda-forge references by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/226
  • fix: order of uid, ds cols by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/228
  • Fix nbdev version by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/227
  • fix: numpy smape by @FedericoGarza in https://github.com/Nixtla/neuralforecast/pull/229

Full Changelog: https://github.com/Nixtla/neuralforecast/compare/v0.0.7...v0.0.8

- Python
Published by FedericoGarza about 4 years ago

https://github.com/nixtla/neuralforecast - Auto ML pipeline

  • Add auto ml pipeline.
  • Add RNN model.
  • Bug fixes.

- Python
Published by FedericoGarza about 4 years ago