Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism
This addresses forecasting challenges in domains like finance and weather, but it is incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackled the problem of forecasting non-stationary time series by proposing the AEFIN framework, which improved accuracy and robustness, outperforming common models in metrics like mean square error and mean absolute error under non-stationary conditions.
Time series forecasting has important applications in financial analysis, weather forecasting, and traffic management. However, existing deep learning models are limited in processing non-stationary time series data because they cannot effectively capture the statistical characteristics that change over time. To address this problem, this paper proposes a new framework, AEFIN, which enhances the information sharing ability between stable and unstable components by introducing a cross-attention mechanism, and combines Fourier analysis networks with MLP to deeply explore the seasonal patterns and trend characteristics in unstable components. In addition, we design a new loss function that combines time-domain stability constraints, time-domain instability constraints, and frequency-domain stability constraints to improve the accuracy and robustness of forecasting. Experimental results show that AEFIN outperforms the most common models in terms of mean square error and mean absolute error, especially under non-stationary data conditions, and shows excellent forecasting capabilities. This paper provides an innovative solution for the modeling and forecasting of non-stationary time series data, and contributes to the research of deep learning for complex time series.