KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction

• URL: http://arxiv.org/abs/2412.05421v1
• Date: Fri, 06 Dec 2024 21:07:11 GMT
• Title: KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction
• Authors: Zhenkai Qin, Baozhong Wei, Caifeng Gao, Jianyuan Ni,
• Abstract summary: Time series forecasting is a critical task in domains such as energy, finance, and meteorology. We propose KEDformer, a knowledge extraction-driven framework that integrates seasonal-trend decomposition. This decomposition enhances the model's ability to capture both short-term fluctuations and long-term patterns.
• Score: 1.224954637705144
• License:
• Abstract: 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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