WDAN uses wavelet decomposition to split series into trend and residual, normalizes them separately, and predicts future statistics to improve non-stationary time series forecasting.
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Wavelet-based Disentangled Adaptive Normalization for Non-stationary Times Series Forecasting
WDAN uses wavelet decomposition to split series into trend and residual, normalizes them separately, and predicts future statistics to improve non-stationary time series forecasting.