  Time Series Forecasting plays a crucial role in various fields such as
industrial equipment maintenance, meteorology, energy consumption, traffic flow
and financial investment. However, despite their considerable advantages over
traditional statistical approaches, current deep learning-based predictive
models often exhibit a significant deviation between their forecasting outcomes
and the ground truth. This discrepancy is largely due to an insufficient
emphasis on extracting the sequence's latent information, particularly its
global information within the frequency domain and the relationship between
different variables. To address this issue, we propose a novel model
Frequency-domain Attention In Two Horizons, which decomposes time series into
trend and seasonal components using a multi-scale sequence adaptive
decomposition and fusion architecture, and processes them separately. FAITH
utilizes Frequency Channel feature Extraction Module and Frequency Temporal
feature Extraction Module to capture inter-channel relationships and temporal
global information in the sequence, significantly improving its ability to
handle long-term dependencies and complex patterns. Furthermore, FAITH achieves
theoretically linear complexity by modifying the time-frequency domain
transformation method, effectively reducing computational costs. Extensive
experiments on 6 benchmarks for long-term forecasting and 3 benchmarks for
short-term forecasting demonstrate that FAITH outperforms existing models in
many fields, such as electricity, weather and traffic, proving its
effectiveness and superiority both in long-term and short-term time series
forecasting tasks. Our codes and data are available at
https://github.com/LRQ577/FAITH.
