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.

基于全 MLP 框架，WindowMixer 模型通过窗口视角检查时间序列的时间变化，将时间序列分解为趋势和季节性组件，并使用 Intra-Window-Mixer 和 Inter-Window-Mixer 模块处理窗口间和窗口内的关系，从而捕捉数据中的复杂模式和长期依赖关系，实验证明 WindowMixer 在长期和短期预测任务中始终优于现有方法。

WindowMixer：时间序列预测中的窗口内和窗口间建模

WindowMixer: Intra-Window and Inter-Window Modeling for Time Series  Forecasting

Long-term time series forecasting is a vital task and has a wide range of
real applications. Recent methods focus on capturing the underlying patterns
from one single domain (e.g. the time domain or the frequency domain), and have
not taken a holistic view to process long-term time series from the
time-frequency domains. In this paper, we propose a Time-Frequency Enhanced
Decomposed Network (TFDNet) to capture both the long-term underlying patterns
and temporal periodicity from the time-frequency domain. In TFDNet, we devise a
multi-scale time-frequency enhanced encoder backbone and develop two separate
trend and seasonal time-frequency blocks to capture the distinct patterns
within the decomposed trend and seasonal components in multi-resolutions.
Diverse kernel learning strategies of the kernel operations in time-frequency
blocks have been explored, by investigating and incorporating the potential
different channel-wise correlation patterns of multivariate time series.
Experimental evaluation of eight datasets from five benchmark domains
demonstrated that TFDNet is superior to state-of-the-art approaches in both
effectiveness and efficiency.

提出了一种 Time-Frequency Enhanced Decomposed Network (TFDNet) 方法，用于从时频域捕捉长期潜在模式和时间周期性，通过多尺度的时频增强编码器支撑和两个独立的趋势和季节时频块来捕捉多分辨率中分解的趋势和季节成分的不同模式，实验证明 TFDNet 在效果和效率上优于现有方法。