---
license: mit
tags:
  - time-series
  - forecasting
  - spiking-neural-networks
  - graph-neural-networks
  - multivariate-time-series
---

# SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting

[![arXiv](https://img.shields.io/badge/arXiv-2606.13901-b31b1b.svg)](https://arxiv.org/abs/2606.13901)
[![ECML PKDD 2026](https://img.shields.io/badge/ECML%20PKDD-2026-blue.svg)](https://arxiv.org/abs/2606.13901)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

📄 **Paper (arXiv):** https://arxiv.org/abs/2606.13901  
💻 **GitHub:** https://github.com/jafarbakhshaliyev/SpikF-GO

Official implementation of **SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting**, accepted to the **ECML PKDD 2026 Research Track**.

![SpikF-GO architecture](assets/spikf-go-architecture.png)

---

## Abstract

SpikF-GO is a spiking neural architecture for multivariate time series forecasting. It combines the hypervariate graph formulation of FourierGNN with spike-driven Fourier-domain graph processing, enabling joint modeling of intra-series temporal dependencies, inter-series dependencies, and time-varying cross-variable interactions. The model introduces sparse frequency selection and Complex LIF-based spectral gating to preserve event-driven computation in the Fourier domain. We also provide **SpikF-GO w/ CPG**, which incorporates Central Pattern Generator-based positional signals for improved long-range temporal modeling.

---

## Key Contributions

- **Graph-based SNN forecasting:** SpikF-GO brings hypervariate graph modeling into SNN-based multivariate time series forecasting.
- **Spike-driven Fourier graph operators:** The model combines sparse frequency gating with Complex LIF-based spectral processing to preserve event-driven computation in the Fourier domain.
- **Unified SNN benchmark:** We evaluate SpikF-GO against major SNN forecasting families under a common experimental protocol across eight benchmark datasets.
- **Energy-aware forecasting:** SpikF-GO achieves competitive-to-superior forecasting performance while reducing theoretical energy consumption relative to FourierGNN.

---

## Related Library: SpikingTSF

We also maintain **[SpikingTSF](https://github.com/spikora/SpikingTSF)**, a broader open-source benchmark library for spiking neural network-based time series forecasting. SpikingTSF unifies SNN forecasting architectures and ANN baselines under a common training and evaluation protocol across datasets, horizons, metrics, and random seeds.

> **Note:** SpikingTSF is a benchmarking library and may not reproduce all experiments from this repository directly.

---

## Repository Structure

```
SpikF-GO/
├── README.md
├── LICENSE
├── CITATION.cff
├── requirements.txt
├── train.py                  # main training & evaluation entry point
├── model/                    # SpikF-GO + all baseline implementations
├── utils/                    # shared utilities (metrics, helpers)
├── data/
│   └── data_loader.py        # dataset loading (raw files placed here at runtime)
├── scripts/
│   ├── ecg.sh
│   ├── covid.sh
│   ├── solar.sh
│   ├── ecl.sh
│   ├── traffic.sh
│   ├── metr_la.sh
│   ├── pems_bay.sh
│   └── wiki.sh
└── assets/
    ├── spikf-go-architecture.png
    └── supplementary.pdf
```

---

## Environment Setup

Create and activate a virtual environment:

**Linux / macOS**
```bash
python3 -m venv venv
source venv/bin/activate
```

**Windows**
```bash
python -m venv venv
venv\Scripts\activate
```

Install dependencies:
```bash
pip install -r requirements.txt
```

Experiments were run with **PyTorch 2.5.1** on a single **NVIDIA RTX 4090**.

---

## Dataset

Download the processed datasets from Figshare:

https://figshare.com/s/7617530bce306584fe95?file=62576929

Place all dataset files **directly** inside the `data/` folder (do **not** create subfolders):

```
SpikF-GO/
├── data/
│   ├── dataset_file_1
│   ├── dataset_file_2
│   └── ...
├── model/
├── scripts/
└── train.py
```

---

## Run Experiments

Scripts are in `scripts/`, one per dataset:

```bash
bash scripts/ecg.sh
bash scripts/covid.sh
bash scripts/solar.sh
bash scripts/ecl.sh
bash scripts/traffic.sh
bash scripts/metr_la.sh
bash scripts/pems_bay.sh
bash scripts/wiki.sh
```

Each script sets the exact hyperparameters used to produce the results reported in the paper.

---

## Supplementary Material

Available at [`assets/supplementary.pdf`](assets/supplementary.pdf).

---

## Citation

If you use this code or build on SpikF-GO, please cite our paper:

**arXiv preprint:**
```bibtex
@misc{bakhshaliyev2026spikfgo,
  title        = {SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting},
  author       = {Bakhshaliyev, Jafar and Landwehr, Niels},
  year         = {2026},
  eprint       = {2606.13901},
  archivePrefix= {arXiv},
  primaryClass = {cs.LG},
  url          = {https://arxiv.org/abs/2606.13901}
}
```

**ECML PKDD 2026 proceedings:**
```bibtex
@inproceedings{bakhshaliyev2026spikfgo,
  title     = {SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting},
  author    = {Bakhshaliyev, Jafar and Landwehr, Niels},
  booktitle = {ECML PKDD},
  year      = {2026}
}
```

See [`CITATION.cff`](CITATION.cff) for full citation metadata.

---

## Acknowledgements

The baselines in `model/` build on prior work. We thank the authors for releasing their code; original licenses are respected.

- **`SpikF.py`** — adapted from **SpikF** (Wu, Huo & Chen, *"SpikF: Spiking Fourier Network for Efficient Long-term Prediction"*, [ICML 2025 / PMLR v267](https://proceedings.mlr.press/v267/wu25m.html)).
- **`TS_Former.py`, `TS_GRU.py`, `TS_TCN.py`** — adapted from **TS-LIF** (Feng et al., *"TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting"*, [arXiv:2503.05108](https://arxiv.org/abs/2503.05108)).
- **`iSpikformer.py`, `SpikeGRU.py`** — adapted from **SeqSNN** (Lv et al., *"Efficient and Effective Time-Series Forecasting with Spiking Neural Networks"*, [arXiv:2402.01533](https://arxiv.org/abs/2402.01533)), [microsoft/SeqSNN](https://github.com/microsoft/SeqSNN).
- **`SpikeRNN_CPG.py`, `SpikeTCN_CPG.py`, `Spikformer_CPG.py`** — CPG variants build on [arXiv:2405.14362](https://arxiv.org/abs/2405.14362) / [microsoft/SeqSNN](https://github.com/microsoft/SeqSNN).
- **`FourierGNN.py`** — adapted from **FourierGNN**, [arXiv:2311.06190](https://arxiv.org/abs/2311.06190) / [aikunyi/FourierGNN](https://github.com/aikunyi/FourierGNN).

---

## License

This project is released under the [MIT License](LICENSE).