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

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend
https://github.com/zalandoresearch/pytorch-ts

deepar lstnet n-beats probabilistic pytorch time-series

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PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

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README 

        
# PyTorchTS



PyTorchTS is a [PyTorch](https://github.com/pytorch/pytorch) Probabilistic Time Series forecasting framework which provides state of the art PyTorch time series models by utilizing [GluonTS](https://github.com/awslabs/gluon-ts) 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](https://github.com/zalandoresearch/pytorch-ts/blob/master/examples/images/readme_0.png?raw=true)



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",

                            prediction_length=12,

                            input_size=19,

                            trainer=Trainer(epochs=10,

                                            device=device))

predictor = estimator.train(training_data=training_data, num_workers=4)

```

```

    45it [00:01, 37.60it/s, avg_epoch_loss=4.64, epoch=0]

    48it [00:01, 39.56it/s, avg_epoch_loss=4.2, epoch=1] 

    45it [00:01, 38.11it/s, avg_epoch_loss=4.1, epoch=2] 

    43it [00:01, 36.29it/s, avg_epoch_loss=4.05, epoch=3]

    44it [00:01, 35.98it/s, avg_epoch_loss=4.03, epoch=4]

    48it [00:01, 39.48it/s, avg_epoch_loss=4.01, epoch=5]

    48it [00:01, 38.65it/s, avg_epoch_loss=4, epoch=6]   

    46it [00:01, 37.12it/s, avg_epoch_loss=3.99, epoch=7]

    48it [00:01, 38.86it/s, avg_epoch_loss=3.98, epoch=8]

    48it [00:01, 39.49it/s, avg_epoch_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](https://github.com/zalandoresearch/pytorch-ts/blob/master/examples/images/readme_1.png?raw=true)



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](https://arxiv.org/abs/2002.06103)

```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

](http://proceedings.mlr.press/v139/rasul21a.html)

```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](https://arxiv.org/abs/2107.03743)

```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}

}

```