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ltsf: A Python package for easy implementation and testing of top models for long-term time-series forecasting.

License: Apache License 2.0

Python 100.00%
data-analysis long-term-prediction machine-learning model-training python state-of-the-art-models time-series-analysis time-series-forecasting

ltsf's Introduction

๐Ÿš€ LTSF: A Baseline Aggregator for Long-Term Time-Series Forecasting

PyPI version

LTSF is a Python ๐Ÿ package that simplifies the process of implementing and testing various state-of-the-art models ๐Ÿง  for long-term time-series forecasting ๐Ÿ“ˆ. This package supports the majority of current leading baselines, offering a user-friendly interface to perform LTSF tasks effortlessly.

LTSF enables users to employ their desired model with a single-line configuration and provides the tools ๐Ÿ› ๏ธ to create training, validation, and testing datasets effortlessly. By further utilizing LTSF's training interface, users can train and test their models using only a few lines of code.

๐Ÿ”ง Supported Baselines

The table below shows the supported baselines with their corresponding references and links to their original implementation:

Baseline Reference Code
PatchTST ICLR 2023 PatchTST
MICN ICLR 2023 MICN
FiLM NIPS 2022 FiLM
TimesNet ICLR 2023 TimesNet
Crossformer ICLR 2023 Crossformer
DLinear AAAI 2023 DLinear
LightTS arXiv 2022 LightTS
ETSformer arXiv 2022 ETSformer
Non-stationary Transformer NeurIPS 2022 Non-stationary Transformer
FEDformer ICML 2022 FEDformer
Pyraformer ICLR 2022 Pyraformer
Autoformer NeurIPS 2021 Autoformer
Informer AAAI 2021 Informer
Reformer ICLR 2020 Reformer
Transformer NeurIPS 2017 Transformer

๐Ÿ“š Usage

Here is an example of how you can use LTSF:

pip install ltsf
import ltsf

# Create a configuration for your desired model and dataset
config = ltsf.Config("Autoformer", "ETTh1") 

# Set your custom configuration
config.set_config({"use_gpu":False})

# Create training, validation, and testing datasets
train_loader, val_loader, test_loader = ltsf.DatasetFactory.create(config, download=True, data_path=".")  

# Create an LTSFTrainer
trainer = ltsf.LTSFTrainer(config) 

# Start the training process
trainer.train(train_loader, val_loader, test_loader)

# Test the trained model
trainer.test(test_loader, res_dir="./result")

โญ Key Features

  • Supports 15 leading baselines for long-term time-series forecasting.
  • User-friendly, enabling model training and testing with minimal lines of code.

๐ŸŽ‰ Update Notes

  • [2023-06-21] LTSF has now been released! ๐Ÿš€

๐Ÿ“ˆ Future Work

  • Planning to support more intelligent parameter settings for improved usability and flexibility.
  • Support custom dataset.

๐Ÿ™ Acknowledgments

We are grateful to the authors of all the papers and their original implementations that made this package possible.

๐Ÿ“œ License

Apache License.

ltsf's People

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