  Time series forecasting (TSF) is crucial in fields like economic forecasting,
weather prediction, traffic flow analysis, and public health surveillance.
Real-world time series data often include noise, outliers, and missing values,
making accurate forecasting challenging. Traditional methods model
point-to-point relationships, which limits their ability to capture complex
temporal patterns and increases their susceptibility to noise.To address these
issues, we introduce the WindowMixer model, built on an all-MLP framework.
WindowMixer leverages the continuous nature of time series by examining
temporal variations from a window-based perspective. It decomposes time series
into trend and seasonal components, handling them individually. For trends, a
fully connected (FC) layer makes predictions. For seasonal components, time
windows are projected to produce window tokens, processed by Intra-Window-Mixer
and Inter-Window-Mixer modules. The Intra-Window-Mixer models relationships
within each window, while the Inter-Window-Mixer models relationships between
windows. This approach captures intricate patterns and long-range dependencies
in the data.Experiments show WindowMixer consistently outperforms existing
methods in both long-term and short-term forecasting tasks.
