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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05421 |
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| _version_ | 1866916512631095296 |
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| author | Qin, Zhenkai Wei, Baozhong Gao, Caifeng Ni, Jianyuan |
| author_facet | Qin, Zhenkai Wei, Baozhong Gao, Caifeng Ni, Jianyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05421 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction Qin, Zhenkai Wei, Baozhong Gao, Caifeng Ni, Jianyuan Machine Learning Artificial Intelligence 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. |
| title | KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.05421 |