FreDF: Learning to Forecast in the Frequency Domain
FreDF addresses label autocorrelation in time series forecasting by transforming labels into a frequency domain using FFT. This model-agnostic objective aligns forecasts and labels where correlation is diminished, reducing estimation bias. The method outperforms existing models across various datasets and proves computationally efficient.