FreDF: Learning to Forecast in Frequency Domain
Hao Wang Licheng Pan Zhichao Chen Degui Yang Sen Zhang Yifei Yang Xinggao Liu Haoxuan Li Dacheng Tao
Time series modeling is uniquely challenged by the presence of
autocorrelation in both historical and label sequences. Current research
predominantly focuses on handling autocorrelation within the historical
sequence but often neglects its presence in the label sequence. Specifically,
emerging forecast models mainly conform to the direct forecast (DF) paradigm,
generating multi-step forecasts under the assumption of conditional
independence within the label sequence. This assumption disregards the inherent
autocorrelation in the label sequence, thereby limiting the performance of
DF-based models. In response to this gap, we introduce the Frequency-enhanced
Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation
by learning to forecast in the frequency domain. Our experiments demonstrate
that FreDF substantially outperforms existing state-of-the-art methods
including iTransformer and is compatible with a variety of forecast models.