An Evolutionary Neural Architecture Search-Based Approach for Time Series Forecasting

Time series forecasting (TSF) is one of the most prevalent research topics in artificial intelligence and has garnered significant attention in the research community. In recent years, significant breakthroughs have been made in TSF research, shifting from traditional statistical models to deep learning (DL), and attention-based methods. While attention-based methods excel at capturing global dependencies, they often face challenges in effectively modeling local patterns. Additionally, the design of these networks typically demands substantial human expertise, experimental work, and manual configuration. To address these issues, we propose an Evolutionary Neural Architecture Search for Time Series Forecasting, entitled ENAS-TSF to automate TSF architecture design. Concretely, we propose a novel local/ global context module encoding strategy to define a search space. Each local context module includes various convolutions with different kernel sizes to capture temporal dependencies, aiming to enhance the local features and pattern recognition. Global encoding meanwhile contains attention mechanisms and feedforward layers for global context modeling. We propose an evolutionary neural architecture search approach to identify the optimal ENAS-TSF architectures, achieving the ideal balance of local/ global context modeling. Extensive experiments on common benchmark datasets show that ENAS-TSF achieves competitive performance compared to state-of-the-art methods, demonstrating the proposed framework’s effectiveness.