A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting
This work addresses forecasting challenges in domains like energy and weather, but it is incremental as it builds on existing MLP-based methods with specific enhancements.
The paper tackled long-term time series forecasting by introducing an MLP-based framework that disentangles multi-scale temporal patterns and independently models trend and seasonal components, resulting in a 4.64% improvement in average MAE over the state-of-the-art method TimeMixer on eight benchmarks.
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.