MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting
Journal:
arXiv
Published Date:
Jun 15, 2025
Abstract
Time series forecasting is a critical and practical problem in many
real-world applications, especially for industrial scenarios, where load
forecasting underpins the intelligent operation of modern systems like clouds,
power grids and traffic networks.However, the inherent complexity and dynamics
of these systems present significant challenges. Despite advances in methods
such as pattern recognition and anti-non-stationarity have led to performance
gains, current methods fail to consistently ensure effectiveness across various
system scenarios due to the intertwined issues of complex patterns,
concept-drift, and few-shot problems. To address these challenges
simultaneously, we introduce a novel scheme centered on fundamental waveform,
a.k.a., meta-pattern. Specifically, we develop a unique Meta-pattern Pooling
mechanism to purify and maintain meta-patterns, capturing the nuanced nature of
system loads. Complementing this, the proposed Echo mechanism adaptively
leverages the meta-patterns, enabling a flexible and precise pattern
reconstruction. Our Meta-pattern Echo transformer (MetaEformer) seamlessly
incorporates these mechanisms with the transformer-based predictor, offering
end-to-end efficiency and interpretability of core processes. Demonstrating
superior performance across eight benchmarks under three system scenarios,
MetaEformer marks a significant advantage in accuracy, with a 37% relative
improvement on fifteen state-of-the-art baselines.
