PDMLP: Patch-based Decomposed MLP for Long-Term Time Series Forecasting
CoRR(2024)
Abstract
Recent studies have attempted to refine the Transformer architecture to
demonstrate its effectiveness in Long-Term Time Series Forecasting (LTSF)
tasks. Despite surpassing many linear forecasting models with ever-improving
performance, we remain skeptical of Transformers as a solution for LTSF. We
attribute the effectiveness of these models largely to the adopted Patch
mechanism, which enhances sequence locality to an extent yet fails to fully
address the loss of temporal information inherent to the permutation-invariant
self-attention mechanism. Further investigation suggests that simple linear
layers augmented with the Patch mechanism may outperform complex
Transformer-based LTSF models. Moreover, diverging from models that use channel
independence, our research underscores the importance of cross-variable
interactions in enhancing the performance of multivariate time series
forecasting. The interaction information between variables is highly valuable
but has been misapplied in past studies, leading to suboptimal cross-variable
models. Based on these insights, we propose a novel and simple Patch-based
Decomposed MLP (PDMLP) for LTSF tasks. Specifically, we employ simple moving
averages to extract smooth components and noise-containing residuals from time
series data, engaging in semantic information interchange through channel
mixing and specializing in random noise with channel independence processing.
The PDMLP model consistently achieves state-of-the-art results on several
real-world datasets. We hope this surprising finding will spur new research
directions in the LTSF field and pave the way for more efficient and concise
solutions.
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