  Long-term time series forecasting is a vital task and has a wide range of
real applications. Recent methods focus on capturing the underlying patterns
from one single domain (e.g. the time domain or the frequency domain), and have
not taken a holistic view to process long-term time series from the
time-frequency domains. In this paper, we propose a Time-Frequency Enhanced
Decomposed Network (TFDNet) to capture both the long-term underlying patterns
and temporal periodicity from the time-frequency domain. In TFDNet, we devise a
multi-scale time-frequency enhanced encoder backbone and develop two separate
trend and seasonal time-frequency blocks to capture the distinct patterns
within the decomposed trend and seasonal components in multi-resolutions.
Diverse kernel learning strategies of the kernel operations in time-frequency
blocks have been explored, by investigating and incorporating the potential
different channel-wise correlation patterns of multivariate time series.
Experimental evaluation of eight datasets from five benchmark domains
demonstrated that TFDNet is superior to state-of-the-art approaches in both
effectiveness and efficiency.
