Skip to content

Kolmogorov Arnold Network (KAN) for Time Series Forecasting (TSF)

Notifications You must be signed in to change notification settings

2448845600/EasyTSF

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

28 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

EasyTSF

This project was upgraded from KAN4TSF to EasyTSF (Experiment assistant for your Time-Series Forecasting).

🚩 News (2024.11) KAN4TSF -> EasyTSF, we will support more time series forecasting models.

🚩 News (2024.09) Model Zoo: RMoK, NLinear, DLinear, RLinear, PatchTST, iTransformer, STID, TimeLLM

🚩 News (2024.09) Introduction and Reproduction (in Chinese)

Usage

Environment

Step by Step with Conda:

conda create -n kan4tsf python=3.10
conda activate kan4tsf
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
python -m pip install lightning

or you can just:

pip install -r requirements.txt

Code and Data

ETTh1 and ETTm1 can be downloaded within this project, and other datasets can be downloaded from Baidu Drive or Google Drive.

Running

python train.py -c config/reproduce_conf/RMoK/ETTh1_96for96.py

Cite

If you find this repo useful, please cite our paper:

@inproceedings{han2023are,
  title={KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?},
  author={Xiao Han, Xinfeng Zhang, Yiling Wu, Zhenduo Zhang and Zhe Wu},
  booktitle={arXiv},
  year={2024},
}

About

Kolmogorov Arnold Network (KAN) for Time Series Forecasting (TSF)

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages