A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2505.08199v2
• Date: Fri, 16 May 2025 13:26:32 GMT
• Title: A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting
• Authors: Boshi Gao, Qingjian Ni, Fanbo Ju, Yu Chen, Ziqi Zhao,
• Abstract summary: Long-term time series forecasting (LTSF) offers broad utility in practical settings like energy consumption and weather prediction.<n>This work confronts key issues in LTSF, including the suboptimal use of multi-granularity information.<n>Our method adeptly disentangles complex temporal dynamics using clear, concurrent predictions across various scales.
• Score: 6.344911113059126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term time series forecasting (LTSF) offers broad utility in practical settings like energy consumption and weather prediction. Accurately predicting long-term changes, however, is demanding due to the intricate temporal patterns and inherent multi-scale variations within time series. This work confronts key issues in LTSF, including the suboptimal use of multi-granularity information, the neglect of channel-specific attributes, and the unique nature of trend and seasonal components, by introducing a proficient MLP-based forecasting framework. Our method adeptly disentangles complex temporal dynamics using clear, concurrent predictions across various scales. These multi-scale forecasts are then skillfully integrated through a system that dynamically assigns importance to information from different granularities, sensitive to individual channel characteristics. To manage the specific features of temporal patterns, a two-pronged structure is utilized to model trend and seasonal elements independently. Experimental results on eight LTSF benchmarks demonstrate that MDMixer improves average MAE performance by 4.64% compared to the recent state-of-the-art MLP-based method (TimeMixer), while achieving an effective balance between training efficiency and model interpretability.

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