Metadata-Version: 2.1
Name: pytorchts
Version: 0.3.1
Summary: PyTorch Probabilistic Time Series Modeling framework
Home-page: https://github.com/zalandoresearch/pytorch-ts
Author: Kashif Rasul
Author-email: kashif.rasul@zalando.de
License: MIT
Description: # 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 API (with minimal changes) and for loading, transforming and back-testing time series data sets. Currently the GluonTS code is copied into this repository with changes for PyTorch but eventually GluonTS should become an external requirement.
        
        ## 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=43,
                                    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
        ```
        
        ## 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
        @article{rasul2020tempflow,
            Author = {Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf}
            Title = {Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows},
            Year = {2020},
            archivePrefix = {arXiv},
            eprint = {2002.06103},
        }
        ```
        
        * [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
        ](https://arxiv.org/abs/2101.12072)
        ```tex
        @article{rasul2020timegrad,
            Author = {Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf}
            Title = {Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting},
            Year = {2021},
            archivePrefix = {arXiv},
            eprint = {2101.12072},
        }
        ```
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
