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SeqSNN

A public framework for time-series forecasting with spiking neural networks (SNNs).

Related Papers

• Efficient and Effective Time-Series Forecasting with Spiking Neural Networks, [ICML 2024], (https://arxiv.org/pdf/2402.01533).
• Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators, [NeurIPS 2024], (https://arxiv.org/pdf/2405.14362).

Installation

To install SeqSNN in a new conda environment:

conda create -n SeqSNN python=[3.8, 3.9, 3.10]
conda activate SeqSNN
git clone https://github.com/microsoft/SeqSNN/
cd SeqSNN
pip install .

If you would like to make changes and run your experiments, use:

pip install -e .

Training

Take the iSpikformer model as an example:

python -m SeqSNN.entry.tsforecast exp/forecast/ispikformer/ispikformer_electricity.yml

You can change the yml configuration files as you want.

You can add, remove, or modify your model architecture in forecaster/network/XXX.py.

Datasets

Metr-la and Pems-bay are available at Google Drive or Baidu Yun. Solar and Electricity can be downloaded from (https://github.com/laiguokun/multivariate-time-series-data).

The folder structure of this project is as follows:

SeqSNN
│   README.md 
│   ...
│
└───data
│   │   metr-la.h5
│   │   pems-bay.h5
│   │
│   └───solar-energy
│   │   │   solar_AL.txt
│   │   │   ...
│   │   
│   └───electricity
│   │   │   electricity.txt
│   │   │   ...
│   │   
│   └───traffic
│   │   │   traffic.txt
│   │   │   ...
│
└───forecaster
│   │   ...
│
└───exp
│   │   ...
│
└───outputs
│   │   ...
│

You can change the path of the data file in exp/forecast/dataset/XXX.yml configuration files.

Acknowledgement

This repo is built upon (forecaster)[https://github.com/Arthur-Null/SRD], which is a general time-series forecasting library. We greatly thank @rk2900 and @Arthur-Null for their initial contribution.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.