  Although the Transformer has been the dominant architecture for time series
forecasting tasks in recent years, a fundamental challenge remains: the
permutation-invariant self-attention mechanism within Transformers leads to a
loss of temporal information. To tackle these challenges, we propose
PatchMixer, a novel CNN-based model. It introduces a permutation-variant
convolutional structure to preserve temporal information. Diverging from
conventional CNNs in this field, which often employ multiple scales or numerous
branches, our method relies exclusively on depthwise separable convolutions.
This allows us to extract both local features and global correlations using a
single-scale architecture. Furthermore, we employ dual forecasting heads that
encompass both linear and nonlinear components to better model future curve
trends and details. Our experimental results on seven time-series forecasting
benchmarks indicate that compared with the state-of-the-art method and the
best-performing CNN, PatchMixer yields $3.9\%$ and $21.2\%$ relative
improvements, respectively, while being 2-3x faster than the most advanced
method. We will release our code and model.
