Skip to main content

Deep Learning Models for time series prediction..

Project description

DeepTS_Forecasting

Release Status CI Status Documentation Status

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

Features

  • TODO

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

deepts_forecasting-0.1.2.tar.gz (5.2 MB view details)

Uploaded Source

Built Distribution

deepts_forecasting-0.1.2-py3-none-any.whl (5.3 MB view details)

Uploaded Python 3

File details

Details for the file deepts_forecasting-0.1.2.tar.gz.

File metadata

  • Download URL: deepts_forecasting-0.1.2.tar.gz
  • Upload date:
  • Size: 5.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for deepts_forecasting-0.1.2.tar.gz
Algorithm Hash digest
SHA256 ce71cdcbe846cb676a02919e6b9519c0cca7f76841037f8cc3a24c384b4e0bdd
MD5 f188fbb73a6dffc10c39526b2c2817f8
BLAKE2b-256 c0005baeac36f8b43591ece14dd847b3275f96517d41201a9ea26e012d20edb2

See more details on using hashes here.

File details

Details for the file deepts_forecasting-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for deepts_forecasting-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f912bd8883c33bde35014f5a28059d80d03cf735b6b79f249a713e77a54b681e
MD5 af6d416f6fb237c1ad6771fdc6980382
BLAKE2b-256 4311a4958a3fd2bf9a02cb28a613d48a65045a4a231e190edc13a04c4bdf85b3

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page