KEDformer通过分解季节与趋势，提升长序列预测效率与精度。
KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction
- 分离时间序列的季节与趋势成分，增强建模能力
- 提取自注意力中关键权重，降低计算开销
- 在能源、交通、气象数据上表现优异，适合长时序预测
时间序列预测在能源、金融和气象等领域至关重要，准确的长期预测尤为关键。尽管基于Transformer的模型在捕捉时间依赖性方面表现出色，但其在长序列上的应用受限于计算效率低下和泛化能力不足。本文提出KEDformer，一种基于知识提取的框架，结合季节-趋势分解以解决上述问题。KEDformer利用知识提取方法聚焦自注意力机制中最具信息量的权重，有效降低计算开销；同时将时间序列解耦为季节与趋势分量，增强对短期波动与长期模式的捕捉能力。在能源、交通和气象领域的五个公开数据集上的大量实验表明，KEDformer具有优异的性能与竞争力，为长时序预测提供高效解决方案。
原文摘要 · Abstract (English)
Time series forecasting is a critical task in domains such as energy, finance, and meteorology, where accurate long-term predictions are essential. While Transformer-based models have shown promise in capturing temporal dependencies, their application to extended sequences is limited by computational inefficiencies and limited generalization. In this study, we propose KEDformer, a knowledge extraction-driven framework that integrates seasonal-trend decomposition to address these challenges. KEDformer leverages knowledge extraction methods that focus on the most informative weights within the self-attention mechanism to reduce computational overhead. Additionally, the proposed KEDformer framework decouples time series into seasonal and trend components. This decomposition enhances the model's ability to capture both short-term fluctuations and long-term patterns. Extensive experiments on five public datasets from energy, transportation, and weather domains demonstrate the effectiveness and competitiveness of KEDformer, providing an efficient solution for long-term time series forecasting.
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