ITS-Mina: A Harris Hawks Optimization-Based All-MLP Framework with Iterative Refinement and External Attention for Multivariate Time Series Forecasting

2026-04-30

AI Summary

This work addresses the high computational cost of Transformer-based models in multivariate time series forecasting by proposing a pure MLP architecture that progressively refines predictions through iterative residual mixers. To efficiently capture cross-variable dependencies, the model incorporates an external attention mechanism with linear complexity, built upon learnable memory units. Additionally, the Harris Hawks Optimization (HHO) algorithm is employed to automatically tune critical hyperparameters, such as the dropout rate. Evaluated on six benchmark datasets, the proposed method significantly outperforms eleven state-of-the-art baseline models, achieving both superior prediction accuracy and computational efficiency.

Abstract

Multivariate time series forecasting plays a pivotal role in numerous real-world applications, including financial analysis, energy management, and traffic planning. While Transformer-based architectures have gained popularity for this task, recent studies reveal that simpler MLP-based models can achieve competitive or superior performance with significantly reduced computational cost. In this paper, we propose ITS-Mina, a novel all-MLP framework for multivariate time series forecasting that integrates three key innovations: (1) an iterative refinement mechanism that progressively enhances temporal representations by repeatedly applying a shared-parameter residual mixer stack, effectively deepening the model's computational capacity without multiplying the number of distinct parameters; (2) an external attention module that replaces traditional self-attention with learnable memory units, capturing cross-sample global dependencies at linear computational complexity; and (3) a Harris Hawks Optimization (HHO) algorithm for automatic dropout rate tuning, enabling adaptive regularization tailored to each dataset. Extensive experiments on six widely-used benchmark datasets demonstrate that ITS-Mina achieves state-of-the-art or highly competitive performance compared to eleven baseline models across multiple forecasting horizons.

Problem

• multivariate time series forecasting
• time series prediction
• MLP-based models
• computational efficiency
• forecasting accuracy

Innovation

• Iterative Refinement
• External Attention
• All-MLP Architecture
• Harris Hawks Optimization
• Multivariate Time Series Forecasting