ARFNet: Scale-aware adaptive receptive field network for time series forecasting

Abstract

Time series forecasting plays a crucial role in decision-making across various scenarios, including weather, traffic, and finance. Real-world time series typically exhibit complex patterns where multiple periods and dynamic trends overlap. However, traditional models based on fixed receptive fields struggle to adapt flexibly to scale variations in periodic and trend features across different application scenarios, thereby limiting their generalization capabilities. In this paper, we propose a scale-aware Adaptive Receptive Field Network (ARFNet). Specifically, we propose an Adaptive Receptive Field Pyramid module that dynamically adjusts the receptive field size based on the inherent characteristics of the data, providing high-quality periodic and trend feature inputs for subsequent analysis. In addition, we design a Scale-Aware Feature Synthesizer module to capture the dependencies between features at different scales, enhancing the model’s comprehension and utilization of multi-scale temporal information. Extensive experimental results on real-world datasets show that ARFNet outperforms state-of-the-art methods in time series forecasting.