FreDF: Learning to Forecast in the Frequency Domain

Excerpt

arXiv:2402.02399v3 Announce Type: replace Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation within historical data, the correlations among future labels are often overlooked. Specifically, modern forecasting models primarily adhere to the Direct Forecast (DF) paradigm, generating multi-step forecasts independently and disregarding label autocorrelation over