MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series Forecasting

Long-term Time Series Forecasting (LTSF) is critical for numerous real-world applications, such as electricity consumption planning, financial forecasting, and disease propagation analysis. LTSF requires capturing long-range dependencies between inputs and outputs, which poses significant challenges due to complex temporal dynamics and high computational demands. While linear models reduce model complexity by employing frequency domain decomposition, current approaches often assume stationarity and filter out high-frequency components that may contain crucial short-term fluctuations. In this paper, we introduce MMFNet, a novel model designed to enhance long-term multivariate forecasting by leveraging a multi-scale masked frequency decomposition approach. Extensive experimentation with benchmark datasets shows that MMFNet not only addresses the limitations of the existing methods but also consistently achieves good performance. Specifically, MMFNet achieves up to 6.0% reductions in the Mean Squared Error (MSE) compared to state-of-the-art models designed for multivariate forecasting tasks. Time series forecasting is pivotal in a wide range of domains, such as environmental monitoring (Bhandari et al., 2017), electrical grid management (Zufferey et al., 2017), financial analysis (Sezer et al., 2020), and healthcare (Zeroual et al., 2020). Accurate long-term forecasting is essential for informed decision-making and strategic planning. Traditional methods, such as autoregressive (AR) models (Nassar et al., 2004), exponential smoothing (Hyndman & Athanasopoulos, 2008), and structural time series models (Harvey, 1989), have provided a robust foundation for time series analysis by leveraging historical data to predict future values. However, real-world systems frequently exhibit complex, non-stationary behavior, with time series characterized by intricate patterns such as trends, fluctuations, and cycles.