FreDF: Learning to Forecast in the Frequency Domain
Hao Wang Lichen Pan Yuan Shen Zhichao Chen Degui Yang Yifei Yang Sen Zhang Xinggao Liu Haoxuan Li Dacheng Tao
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 correlations over time. In this work, we demonstrate that the learning objective of DF is biased in the presence of label correlation. To address this issue, we propose the Frequency-enhanced Direct Forecast (FreDF), which mitigates label correlation by learning to forecast in the frequency domain, thereby reducing estimation bias. Our experiments show that FreDF significantly outperforms existing state-of-the-art methods and is compatible with a variety of forecast models. Code is available at https://github.com/Master-PLC/FreDF.
Time series, Long-term Forecast
Learning to forecast in the frequency domain improves forecasting performance.