DeepTS_Forecasting

Deepts_forecasting is a Easy-to-use package for time series forecasting with deep Learning models. It contains a variety of models, from classics such as seq2seq to more complex deep neural networks. The models can all be used in the same way, using fit() and predict() functions,

• Free software: MIT

Documentation

• https://yunxileo.github.io/deepts_forecasting/

Features

• Univariate model and multivariate model
• Point estimation and interval estimation
• Unified modelling with Multiple items

Models list

Model	Paper
Seq2Seq	Sequence to Sequence Learning with Neural Networks
DeepAR	DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Lstnet	Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
MQ-RNN	A Multi-Horizon Quantile Recurrent Forecaster
N-Beats	N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
TCN	An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Transformer	Attention Is All You Need
Informer	Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Autoformer	Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
TFT	Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
MAE	Masked Autoencoders Are Scalable Vision Learners

LICENSE

This project is licensed under the MIT License - see the LICENSE file for details.

Credits

This package was created with Cookiecutter and the zillionare/cookiecutter-pypackage project template.