MDMixer improves long-term time series forecasting by generating parallel multi-granularity predictions, mixing them from coarse to fine, and adaptively weighting each channel's scales, reaching a 4.64% average MAE reduction over TimeMixer on eight benchmarks.
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A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting
MDMixer improves long-term time series forecasting by generating parallel multi-granularity predictions, mixing them from coarse to fine, and adaptively weighting each channel's scales, reaching a 4.64% average MAE reduction over TimeMixer on eight benchmarks.