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ST-SSDL

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[NeurIPS-25] How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning

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# <div align="center">[NeurIPS 2025] How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning </div>

* ### ST-SSDL Framework:

![Framework](https://raw.githubusercontent.com/Jimmy-7664/ST-SSDL/main/fig2_v2.jpg)

## Preprint Link 
[![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=ST-SSDL&color=red&logo=arxiv)](https://arxiv.org/abs/2510.04908)
* ### OS

Linux systems (*e.g.* Ubuntu and CentOS).

* ### Python

The code is built based on Python 3.9. You can install required packages using pip:

```
  pip install -r requirements.txt
  ```

* ### Datasets

All six datasets are already provided. The PEMS-BAY dataset is provided in zip file due to the limitation of file size. You just need to unzip the file in the PEMSBAY folder.
  
* Run following commands to prepare data:

```bash
  python generate_training_data_his_BAY.py
  python generate_training_data_his_LA.py
  python generate_training_data_his_PEMS.py --dataset PEMS04
  python generate_training_data_his_PEMS.py --dataset PEMS07
  python generate_training_data_his_PEMS.py --dataset PEMS08
  python generate_training_data_his_D7.py --dataset PEMSD7M
  ```

* Then train the model with following commands:

```bash
  cd model_STSSDL
  python train_STSSDL.py --gpu 0 --dataset METRLA
  python train_STSSDL.py --gpu 0 --dataset PEMSBAY
  python train_STSSDL.py --gpu 0 --dataset PEMSD7M
  python train_STSSDL.py --gpu 0 --dataset PEMS04
  python train_STSSDL.py --gpu 0 --dataset PEMS07
  python train_STSSDL.py --gpu 0 --dataset PEMS08
  
  ```

##  Performance on Spatiotemporal Forecasting Benchmarks

![Main results.](https://raw.githubusercontent.com/Jimmy-7664/ST-SSDL/main/result.png)

ST-SSDL

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