SSCNN

Welcome to the official repository of the SSCNN paper: Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting.

[Poster]

Model Implementation

SSCNN’s performance can be influenced by the connection layout of the four normalization blocks at each layer. We provide two connection strategies: series and parallel. Specify the desired connection by setting the connection parameter to "series" or "parallel" in the script.

Each normalization block or attention operation can also be enabled or disabled by setting its respective parameter to 1 (enabled) or 0 (disabled), providing flexibility for experimentation.

Getting Started

Environment Requirements

To get started, ensure you have Conda installed on your system and follow these steps to set up the environment:

conda create -n SSCNN python=3.8
conda activate SSCNN
pip install -r requirements.txt

Data Preparation

All the datasets needed for SSCNN can be obtained from the Google Drive provided in Autoformer. Create a separate folder named ./dataset and place all the CSV files in this directory. Note: Place the CSV files directly into this directory, such as "./dataset/ETTh1.csv"

Training Example

You can specify separate scripts to run independent tasks, such as obtaining results on electricity:

sh scripts/long_term_forecast/ECL_script/SSCNN.sh

Citation

@inproceedings{deng2024parsimony,
  title={Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting},
  author={Deng, Jinliang and Ye, Feiyang and Yin, Du and Song, Xuan and Tsang, Ivor and Xiong, Hui},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024}
}

Acknowledgement

We extend our heartfelt appreciation to the following GitHub repositories for providing valuable code bases and datasets:

https://github.com/yuqinie98/patchtst

https://github.com/zhouhaoyi/Informer2020

https://github.com/thuml/Time-Series-Library