pytorchts

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

https://github.com/zalandoresearch/pytorch-ts

Science Score: 54.0%

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Keywords

deepar lstnet n-beats probabilistic pytorch time-series

Keywords from Contributors

transformer

Last synced: 6 months ago · JSON representation ·

Repository

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

Basic Info

Host: GitHub
Owner: zalandoresearch
License: mit
Language: Python
Default Branch: master
Homepage:
Size: 3.74 MB

Statistics

Stars: 1,326
Watchers: 24
Forks: 200
Open Issues: 103
Releases: 9

Topics

deepar lstnet n-beats probabilistic pytorch time-series

Created almost 6 years ago · Last pushed over 1 year ago

Metadata Files

Readme License Citation

PyTorchTS

PyTorchTS is a PyTorch Probabilistic Time Series forecasting framework which provides state of the art PyTorch time series models by utilizing GluonTS as its back-end API and for loading, transforming and back-testing time series data sets.

Installation

$ pip3 install pytorchts

Quick start

Here we highlight the the API changes via the GluonTS README.

```python import matplotlib.pyplot as plt import pandas as pd import torch

from gluonts.dataset.common import ListDataset from gluonts.dataset.util import to_pandas

from pts.model.deepar import DeepAREstimator from pts import Trainer ```

This simple example illustrates how to train a model on some data, and then use it to make predictions. As a first step, we need to collect some data: in this example we will use the volume of tweets mentioning the AMZN ticker symbol.

python url = "https://raw.githubusercontent.com/numenta/NAB/master/data/realTweets/Twitter_volume_AMZN.csv" df = pd.read_csv(url, header=0, index_col=0, parse_dates=True)

The first 100 data points look like follows:

python df[:100].plot(linewidth=2) plt.grid(which='both') plt.show()

png

We can now prepare a training dataset for our model to train on. Datasets are essentially iterable collections of dictionaries: each dictionary represents a time series with possibly associated features. For this example, we only have one entry, specified by the "start" field which is the timestamp of the first data point, and the "target" field containing time series data. For training, we will use data up to midnight on April 5th, 2015.

python training_data = ListDataset( [{"start": df.index[0], "target": df.value[:"2015-04-05 00:00:00"]}], freq = "5min" )

A forecasting model is a predictor object. One way of obtaining predictors is by training a correspondent estimator. Instantiating an estimator requires specifying the frequency of the time series that it will handle, as well as the number of time steps to predict. In our example we're using 5 minutes data, so req="5min", and we will train a model to predict the next hour, so prediction_length=12. The input to the model will be a vector of size input_size=43 at each time point. We also specify some minimal training options in particular training on a device for epoch=10.

```python device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

estimator = DeepAREstimator(freq="5min", predictionlength=12, inputsize=19, trainer=Trainer(epochs=10, device=device)) predictor = estimator.train(trainingdata=trainingdata, numworkers=4) 45it [00:01, 37.60it/s, avgepochloss=4.64, epoch=0] 48it [00:01, 39.56it/s, avgepochloss=4.2, epoch=1] 45it [00:01, 38.11it/s, avgepochloss=4.1, epoch=2] 43it [00:01, 36.29it/s, avgepochloss=4.05, epoch=3] 44it [00:01, 35.98it/s, avgepochloss=4.03, epoch=4] 48it [00:01, 39.48it/s, avgepochloss=4.01, epoch=5] 48it [00:01, 38.65it/s, avgepochloss=4, epoch=6]
46it [00:01, 37.12it/s, avgepochloss=3.99, epoch=7] 48it [00:01, 38.86it/s, avgepochloss=3.98, epoch=8] 48it [00:01, 39.49it/s, avgepoch_loss=3.97, epoch=9] ```

During training, useful information about the progress will be displayed. To get a full overview of the available options, please refer to the source code of DeepAREstimator (or other estimators) and Trainer.

We're now ready to make predictions: we will forecast the hour following the midnight on April 15th, 2015.

python test_data = ListDataset( [{"start": df.index[0], "target": df.value[:"2015-04-15 00:00:00"]}], freq = "5min" )

python for test_entry, forecast in zip(test_data, predictor.predict(test_data)): to_pandas(test_entry)[-60:].plot(linewidth=2) forecast.plot(color='g', prediction_intervals=[50.0, 90.0]) plt.grid(which='both')

png

Note that the forecast is displayed in terms of a probability distribution: the shaded areas represent the 50% and 90% prediction intervals, respectively, centered around the median (dark green line).

Development

pip install -e . pytest test

Citing

To cite this repository:

tex @software{pytorchgithub, author = {Kashif Rasul}, title = {{P}yTorch{TS}}, url = {https://github.com/zalandoresearch/pytorch-ts}, version = {0.6.x}, year = {2021}, }

Scientific Article

We have implemented the following model using this framework:

Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows tex @INPROCEEDINGS{rasul2020tempflow, author = {Kashif Rasul and Abdul-Saboor Sheikh and Ingmar Schuster and Urs Bergmann and Roland Vollgraf}, title = {{M}ultivariate {P}robabilistic {T}ime {S}eries {F}orecasting via {C}onditioned {N}ormalizing {F}lows}, year = {2021}, url = {https://openreview.net/forum?id=WiGQBFuVRv}, booktitle = {International Conference on Learning Representations 2021}, }
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting tex @InProceedings{pmlr-v139-rasul21a, title = {{A}utoregressive {D}enoising {D}iffusion {M}odels for {M}ultivariate {P}robabilistic {T}ime {S}eries {F}orecasting}, author = {Rasul, Kashif and Seward, Calvin and Schuster, Ingmar and Vollgraf, Roland}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8857--8868}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/rasul21a/rasul21a.pdf}, url = {http://proceedings.mlr.press/v139/rasul21a.html}, }
Probabilistic Time Series Forecasting with Implicit Quantile Networks tex @misc{gouttes2021probabilistic, title={{P}robabilistic {T}ime {S}eries {F}orecasting with {I}mplicit {Q}uantile {N}etworks}, author={Adèle Gouttes and Kashif Rasul and Mateusz Koren and Johannes Stephan and Tofigh Naghibi}, year={2021}, eprint={2107.03743}, archivePrefix={arXiv}, primaryClass={cs.LG} }

Owner

Name: Zalando Research
Login: zalandoresearch
Kind: organization
Email: research@zalando.de
Location: Berlin, Germany

Website: https://research.zalando.com/
Repositories: 9
Profile: https://github.com/zalandoresearch

Repositories of the research branch of Zalando SE

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite it using the following metadata
title: PyTorchTS
authors: 
- family-names: Rasul
  given-names: Kashif
license: MIT
repository-code: https://github.com/zalandoresearch/pytorch-ts
version: 0.6.0

GitHub Events

Total

Issues event: 18
Watch event: 89
Issue comment event: 20
Fork event: 12

Last Year

Issues event: 18
Watch event: 89
Issue comment event: 20
Fork event: 12

Committers

Last synced: 11 months ago

All Time

Total Commits: 432
Total Committers: 15
Avg Commits per committer: 28.8
Development Distribution Score (DDS): 0.171

Past Year

Commits: 1
Committers: 1
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Dr. Kashif Rasul	k**l@g**m	358
Kashif Rasul	k**l@z**e	54
Edrin Basha	e**a@z**e	5
Vahe Hakobyan	v**n@z**e	3
Ingmar Schuster	i**r@z**e	2
ssmall41	s****1	1
aslinagy	6****y	1
Shoaib Burq	s**q@g**m	1
Shashank Deshpande	s**8@g**m	1
Samuel Norling	n**l@g**m	1
NielsRogge	4****e	1
Nick	n**k@k**a	1
Larkin Liu	l**n@g**m	1
Clara Grotehans	6****s	1
Adele Gouttes	a**s@z**e	1

Committer Domains (Top 20 + Academic)

zalando.de: 5 kousu.ca: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 134
Total pull requests: 18
Average time to close issues: 17 days
Average time to close pull requests: 10 days
Total issue authors: 105
Total pull request authors: 12
Average comments per issue: 2.66
Average comments per pull request: 1.06
Merged pull requests: 9
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 14
Pull requests: 0
Average time to close issues: about 13 hours
Average time to close pull requests: N/A
Issue authors: 9
Pull request authors: 0
Average comments per issue: 0.64
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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hanlaoshi (4)
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Pull Request Authors

kashif (5)
larkz (3)
janrth (1)
LemonCANDY42 (1)
khuyentran1401 (1)
nonconvexopt (1)
aslinagy (1)
NielsRogge (1)
Samnor (1)
ClaraGrthns (1)
shashankdeshpande (1)
kousu (1)

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Issue Labels

bug (1) enhancement (1)

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- pypi 129 last-month

Total dependent packages: 1
Total dependent repositories: 9
Total versions: 8
Total maintainers: 1

pypi.org: pytorchts

PyTorch Probabilistic Time Series Modeling framework

Homepage: https://github.com/zalandoresearch/pytorch-ts
Documentation: https://pytorchts.readthedocs.io/
License: MIT
Latest release: 0.6.0
published almost 4 years ago

Versions: 8
Dependent Packages: 1
Dependent Repositories: 9
Downloads: 129 Last month

Rankings

Stargazers count: 2.0%

Forks count: 3.8%

Average: 4.3%

Dependent packages count: 4.7%

Dependent repos count: 4.9%

Downloads: 6.0%

Maintainers (1)

krasul

Last synced: 7 months ago

Dependencies

.github/workflows/publish-to-pypi.yml actions

actions/checkout master composite
actions/setup-python v1 composite
pypa/gh-action-pypi-publish master composite

setup.py pypi

gluonts >=0.9.0
holidays *
matplotlib *
numpy *
pandas *
scipy *
tensorboard *
torch >=1.8.0
tqdm *

pytorchts

Science Score: 54.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

PyTorchTS

Installation

Quick start

Development

Citing

Scientific Article

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

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Issue Labels

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Packages

pypi.org: pytorchts

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Maintainers (1)

Dependencies