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# HaKAN
**Time Series Forecasting with Hahn Kolmogorov-Arnold Networks**

This repository contains the code for our project on long-term time series forecasting.

<h3>Architecture</h3>
The model integrates channel independence, reversible instance normalization, and patching, followed by patch and position embeddings. A stack of R Hahn-KAN blocks, each with intra-patch and inter-patch KAN layers using Hahn polynomials, processes the embedded sequence to capture temporal patterns. The output is mapped through a bottleneck structure with two fully connected layers to produce the final forecast.
<p align="center">
  <img src="Architecture/Arc.png" alt="HaKAN Architecture" width="700"/>
</p>

### 📊 Long-term Forecasting Results (Avg. MSE, MAE)

**Look-back = 96**, averaged over **T ∈ {96, 192, 336, 720}**  
Lower is better.

| Dataset | **HaKAN** | S-Mamba | iTrans | RLinear | PatchTST | Crossf. | TiDE | TimesNet | FEDformer |
|--------|-----------|---------|--------|---------|----------|---------|------|-----------|-----------|
| ETTh1 | **0.439 (0.429)** | 0.455 (0.450) | 0.454 (0.447) | 0.446 (0.434) | 0.469 (0.454) | 0.529 (0.522) | 0.541 (0.507) | 0.458 (0.450) | 0.440 (0.460) |
| ETTh2 | **0.348 (0.383)** | 0.381 (0.405) | 0.383 (0.407) | 0.374 (0.398) | 0.387 (0.407) | 0.942 (0.684) | 0.611 (0.550) | 0.414 (0.427) | 0.437 (0.449) |
| ETTm1 | **0.384 (0.399)** | 0.398 (0.405) | 0.407 (0.410) | 0.414 (0.407) | 0.387 (0.400) | 0.513 (0.496) | 0.419 (0.419) | 0.400 (0.406) | 0.448 (0.452) |
| ETTm2 | **0.276 (0.324)** | 0.288 (0.332) | 0.288 (0.332) | 0.286 (0.327) | 0.281 (0.326) | 0.757 (0.610) | 0.358 (0.404) | 0.291 (0.333) | 0.305 (0.349) |

📌 Format: **MSE (MAE)**.

### Requirements
<pre><code>pip install numpy==1.24.3 matplotlib==3.7.2 pandas==2.0.3 scikit-learn==1.3.0 torch==2.4.1+cu121 </code></pre>

### Datasets
------
The datasets are hosted on Google Drive by Autoformer. Please download them and place them in the `./datasets/` directory before running the experiments.

👉 [Access the Datasets on Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy)

### Experiments
If you want to run an experiment, just run the following script and edit it as you need. This script is for the look-back window is 96.
<pre><code>sh ./scripts/SHORT/etth1.sh</code></pre>
If you want to run an experiment for the look-back window 336, you should run the following script:
<pre><code>sh ./scripts/LONG/etth1.sh</code></pre>

### 🙏 Acknowledgements

We gratefully acknowledge the following repositories and authors whose work inspired or supported our research:

- 🔗 [ZimingLiu/Kolmogorov-Arnold-Network](https://github.com/KindXiaoming/pykan)  
  Provided the foundational implementation of Kolmogorov-Arnold Networks (KANs).

- 🧠 [YuqiNie/PatchTST](https://github.com/yuqinie98/PatchTST)  
  Served as a strong baseline for comparison in multivariate time series forecasting.

---

We appreciate the open-source contributions that helped make this work possible.

### 📄 Citation

If you use this work, please cite:

```bibtex
@inproceedings{Hasanetal-2026-HaKAN,
  title     = {HaKAN: Time Series Forecasting with Hahn Kolmogorov-Arnold Networks},
  author    = {Hasan, Md Zahidul and
               Ben Hamza, Abdessamad and
               Bouguila, Nizar},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  year      = {2026}
}

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