Affine mapping dominates LTSF benchmarks by learning similar input-to-output transition matrices, captures periodic signals well but struggles with non-periodic or cross-channel varying periods; reversible normalization converts trends to periodic-like patterns.
Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid cnn with a lstm-ae based framework
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Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
Affine mapping dominates LTSF benchmarks by learning similar input-to-output transition matrices, captures periodic signals well but struggles with non-periodic or cross-channel varying periods; reversible normalization converts trends to periodic-like patterns.