New Hahn‑KAN Model Advances Multivariate Time Series Forecasting

Global: New Hahn‑KAN Model Advances Multivariate Time Series Forecasting

Researchers Md Zahidul Hasan, A. Ben Hamza, and Nizar Bouguila introduced a novel forecasting framework called HaKAN on January 25, 2026, aiming to address efficiency and interpretability challenges in multivariate time‑series prediction.

Background

Recent advances in long‑term forecasting have been dominated by Transformer‑based and multilayer perceptron (MLP) models. While Transformers achieve strong accuracy, they suffer from quadratic computational complexity and permutation‑equivariant attention mechanisms. MLPs, on the other hand, are prone to spectral bias, limiting their ability to capture intricate temporal dynamics.

Model Architecture

HaKAN leverages Kolmogorov‑Arnold Networks (KANs) enhanced with learnable activation functions derived from Hahn polynomials. The architecture incorporates channel‑independent processing, a patching strategy, and a stack of Hahn‑KAN blocks linked by residual connections. A bottleneck composed of two fully connected layers reduces dimensionality before final output.

The core Hahn‑KAN block combines inter‑patch and intra‑patch KAN layers, enabling the model to capture both global trends and local temporal variations within the data.

Performance Evaluation

Extensive experiments on established forecasting benchmarks reported that HaKAN consistently outperformed recent state‑of‑the‑art methods. The authors highlighted improvements across multiple metrics, demonstrating the model’s robustness on diverse datasets.

Ablation studies further validated the contribution of each architectural component, confirming that the Hahn‑based activations and the residual‑enhanced block design were pivotal to the observed gains.

Implications and Future Directions

By offering a lightweight and interpretable alternative, HaKAN may broaden the applicability of advanced forecasting techniques to resource‑constrained environments and domains requiring model transparency.

The authors suggest that future work will explore scaling the approach to larger time‑series collections and integrating the framework with complementary machine‑learning paradigms.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.