Frequency-domain MLPs are More Effective Learners in Time Series
  Forecasting
Kun Yi Qi Zhang Wei Fan Shoujin Wang Pengyang Wang Hui He Defu Lian Ning An Longbing Cao Zhendong Niu
Time series forecasting has played the key role in different industrial,
including finance, traffic, energy, and healthcare domains. While existing
literatures have designed many sophisticated architectures based on RNNs, GNNs,
or Transformers, another kind of approaches based on multi-layer perceptrons
(MLPs) are proposed with simple structure, low complexity, and {superior
performance}. However, most MLP-based forecasting methods suffer from the
point-wise mappings and information bottleneck, which largely hinders the
forecasting performance. To overcome this problem, we explore a novel direction
of applying MLPs in the frequency domain for time series forecasting. We
investigate the learned patterns of frequency-domain MLPs and discover their
two inherent characteristic benefiting forecasting, (i) global view: frequency
spectrum makes MLPs own a complete view for signals and learn global
dependencies more easily, and (ii) energy compaction: frequency-domain MLPs
concentrate on smaller key part of frequency components with compact signal
energy. Then, we propose FreTS, a simple yet effective architecture built upon
Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two
stages, (i) Domain Conversion, that transforms time-domain signals into complex
numbers of frequency domain; (ii) Frequency Learning, that performs our
redesigned MLPs for the learning of real and imaginary part of frequency
components. The above stages operated on both inter-series and intra-series
scales further contribute to channel-wise and time-wise dependency learning.
Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for
short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate
our consistent superiority over state-of-the-art methods.