GluonTS: Probabilistic Time Series Models in Python
arXiv:1906.05264
Abstract
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.
ICML Time Series Workshop 2019
References in corpus (8)
- Scikit-learn: Machine Learning in Python
- Sequence to Sequence Learning with Neural Networks
- MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
- Variational Inference with Normalizing Flows
- A Multi-Horizon Quantile Recurrent Forecaster
- The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
- Effective Bayesian Modeling of Groups of Related Count Time Series
- Deep Factors with Gaussian Processes for Forecasting
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