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This is a PyTorch implementation of the paper: Towards Online Spatio-Temporal Data Prediction: A Knowledge Distillation Driven Continual Learning Approach (Storm)

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Storm

This is a PyTorch implementation of the paper: Towards Online Spatio-Temporal Data Prediction: A Knowledge Distillation Driven Continual Learning Approach (Storm)

framewwork

Requirements

The model is implemented using Python3 with dependencies specified in requirements.txt

pip install -r requirements.txt

Data Preparation

  1. METR-LA :The METR-LA dataset contains traffic data collected from 207 loop detectors on highways in Los Angeles County. The data spans from March 1, 2012, to June 30, 2012, with a sampling interval of 5 minutes.

  2. PEMS-BAY:The PEMS-BAY dataset consists of traffic data collected by 325 sensors in the California Bay Area. The data spans from January 1, 2017, to May 31, 2017, with a sampling interval of 5 minutes.

  3. PEMS04: This PEMS04 dataset includes traffic data from the PeMS system in California's District 4. It covers the period from January 1, 2018, to February 28, 2018, with a sampling interval of 5 minutes, collected from 307 sensors.

  4. PEMS08:The PEMS08 dataset comprises traffic data from the PeMS system in California's District 8. The data spans from July 1, 2016, to August 31, 2016, sampled every 5 minutes, and collected from 170 sensors.

[METR-LA & PEMS-BAY]:https://github.com/liyaguang/DCRNN

[PEMSD4 & PEMSD8]:https://github.com/Davidham3/ASTGCN


# Create data directories, For example
mkdir -p data/{PEMSD4}

Download PEMSD4 dataset then unzip in PEMSD4

Model Training

  • STGCN
cd STGCN 

python STGCN_Main.py

  • AGCRN
cd AGCRN 

python AGCRN_Main.py

  • MTGNN
cd MTGNN 

python MTGNN_Main.py

  • STAEformer
cd STAEformer 

python STGCN_Main.py

cd model/

python train.py -d <dataset> -g <gpu_id>

Acknowledgement

Our research code refers to the following works:

[1] STG4Traffic:https://github.com/trainingl/STG4Traffic

[2] STAEformer https://github.com/XDZhelheim/STAEformer

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This is a PyTorch implementation of the paper: Towards Online Spatio-Temporal Data Prediction: A Knowledge Distillation Driven Continual Learning Approach (Storm)

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