SCINet

Abstract

1. Introduction

This is the original pytorch implementation for the following paper: SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction. Alse see the Open Review verision.

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2. Objective

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)

This project demonstrates how Python can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Provide all training logs.
• Support RevIN to handle datasets with a large train-test sample distribution gap. To activate, simply add --RIN True to the command line. [Read more]&#40;./docs/RevIN.md&#41;

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/cure-lab/SCINet.git
cd SCINet

python run_financial.py --dataset_name traffic --window_size 168 --horizon 3 --hidden-size 1 --single_step 1 --stacks 2 --levels 3 --lr 5e-4 --dropout 0.5 --batch_size 16 --model_name traf_I168_o3_lr5e-4_bs16_dp0.5_h1_s2l3_w1.0

python run_financial.py --dataset_name traffic --window_size 168 --horizon 6 --hidden-size 2 --single_step 1 --stacks 1 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I168_o6_lr5e-4_bs16_dp0.25_h2_s1l3_w1.0

python run_financial.py --dataset_name traffic --window_size 168 --horizon 12 --hidden-size 0.5 --single_step 1 --stacks 2 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I168_o12_lr5e-4_bs16_dp0.25_h0.5_s2l3_w1.0

python run_financial.py --dataset_name traffic --window_size 168 --horizon 24 --hidden-size 2 --single_step 1 --stacks 2 --levels 2 --lr 5e-4 --dropout 0.5 --batch_size 16 --model_name traf_I168_o24_lr5e-4_bs16_dp0.5_h2_s2l2_w1.0

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by cure-lab and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.