提出多尺度表示框架，提升长期时间序列预测精度
A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting
- 分尺度并行预测，动态融合不同粒度信息
- 在8个基准上平均MAE降低4.64%，优于TimeMixer
- 适合能源、气象等需长期预测的场景
长期时间序列预测（LTSF）在能源消耗、天气预报等实际场景中具有广泛应用。然而，由于时间序列中存在复杂的时间模式和固有的多尺度变化，准确预测长期趋势仍具挑战性。本文针对多粒度信息利用不足、通道特异性忽略以及趋势与季节成分特殊性等问题，提出一种基于MLP的高效预测框架MDMixer。该方法通过在多个尺度上进行清晰的并行预测，解耦复杂的时间动态；再通过动态权重机制，根据各通道特性自适应融合不同粒度的信息。为专门建模趋势与季节成分，采用双分支结构分别处理。在8个LTSF基准上的实验表明，相较于近期最先进的MLP方法TimeMixer，MDMixer平均MAE性能提升4.64%，同时兼顾训练效率与模型可解释性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。