Package: gluonts
GluonTS - Probabilistic Time Series Modeling in PythonGluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models,
based onPyTorchandMXNet.InstallationGluonTS requires Python 3.7 or newer, and the easiest way to install it is viapip:# install with support for torch modelspipinstall"gluonts[torch]"# install with support for mxnet modelspipinstall"gluonts[mxnet]"See thedocumentationfor more info on how GluonTS can be installed.Simple ExampleTo illustrate how to use GluonTS, we train a DeepAR-model and make predictions
using the airpassengers dataset. The dataset consists of a single time
series of monthly passenger numbers between 1949 and 1960. We train the model
on the first nine years and make predictions for the remaining three years.importpandasaspdimportmatplotlib.pyplotaspltfromgluonts.dataset.pandasimportPandasDatasetfromgluonts.dataset.splitimportsplitfromgluonts.torchimportDeepAREstimator# Load data from a CSV file into a PandasDatasetdf=pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/""TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv",index_col=0,parse_dates=True,)dataset=PandasDataset(df,target="#Passengers")# Split the data for training and testingtraining_data,test_gen=split(dataset,offset=-36)test_data=test_gen.generate_instances(prediction_length=12,windows=3)# Train the model and make predictionsmodel=DeepAREstimator(prediction_length=12,freq="M",trainer_kwargs={"max_epochs":5}).train(training_data)forecasts=list(model.predict(test_data.input))# Plot predictionsplt.plot(df["1954":],color="black")forforecastinforecasts:forecast.plot()plt.legend(["True values"],loc="upper left",fontsize="xx-large")plt.show()Note, the forecasts are displayed in terms of a probability distribution and
the shaded areas represent the 50% and 90% prediction intervals.ContributingIf you wish to contribute to the project, please refer to ourcontribution guidelines.CitingIf you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers,
in addition to any model-specific references that are relevant for your work:@article{gluonts_jmlr,author={Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneiderand Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddixand Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella andAli Caner Türkmen and Yuyang Wang},title={{GluonTS: Probabilistic and Neural Time Series Modeling in Python}},journal={Journal of Machine Learning Research},year={2020},volume={21},number={116},pages={1-6},url={http://jmlr.org/papers/v21/19-820.html}}@article{gluonts_arxiv,author={Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. andFlunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C.and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. andTürkmen, A. C. and Wang, Y.},title={{GluonTS: Probabilistic Time Series Modeling in Python}},journal={arXiv preprint arXiv:1906.05264},year={2019}}LinksDocumentationDocumentation (stable)Documentation (development)ReferencesJMLR MLOSS PaperArXiv PaperCollected Papers from the group behind GluonTS: a bibliography.Tutorials and WorkshopsTutorial at IJCAI 2021 (with videos)withYouTube link.Tutorial at WWW 2020 (with videos)Tutorial at SIGMOD 2019Tutorial at KDD 2019Tutorial at VLDB 2018Neural Time Series with GluonTSInternational Symposium of Forecasting: Deep Learning for Forecasting workshop