A public framework for time-series forecasting with spiking neural networks (SNNs).
- Efficient and Effective Time-Series Forecasting with Spiking Neural Network, [ICML 2024], (https://arxiv.org/pdf/2402.01533).
- Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators, [Arxiv:2405.14362], (https://arxiv.org/pdf/2405.14362).
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 own expeirments, use:
pip install -e .
Take the iSpikformer model as an example:
python tsforecast.py 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.
Metr-la and Pems-bay are available at 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 procect 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 data file in exp/forecast/dataset/XXX.yml configuration files.
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.
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.
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