FCDNet: Frequency-Guided Complementary Dependency Modeling for
  Multivariate Time-Series Forecasting
Weijun Chen Heyuan Wang Ye Tian Shijie Guan Ning Liu
Multivariate time-series (MTS) forecasting is a challenging task in many
real-world non-stationary dynamic scenarios. In addition to intra-series
temporal signals, the inter-series dependency also plays a crucial role in
shaping future trends. How to enable the model's awareness of dependency
information has raised substantial research attention. Previous approaches have
either presupposed dependency constraints based on domain knowledge or imposed
them using real-time feature similarity. However, MTS data often exhibit both
enduring long-term static relationships and transient short-term interactions,
which mutually influence their evolving states. It is necessary to recognize
and incorporate the complementary dependencies for more accurate MTS
prediction. The frequency information in time series reflects the evolutionary
rules behind complex temporal dynamics, and different frequency components can
be used to well construct long-term and short-term interactive dependency
structures between variables. To this end, we propose FCDNet, a concise yet
effective framework for multivariate time-series forecasting. Specifically,
FCDNet overcomes the above limitations by applying two light-weight dependency
constructors to help extract long- and short-term dependency information
adaptively from multi-level frequency patterns. With the growth of input
variables, the number of trainable parameters in FCDNet only increases
linearly, which is conducive to the model's scalability and avoids
over-fitting. Additionally, adopting a frequency-based perspective can
effectively mitigate the influence of noise within MTS data, which helps
capture more genuine dependencies. The experimental results on six real-world
datasets from multiple fields show that FCDNet significantly exceeds strong
baselines, with an average improvement of 6.82% on MAE, 4.98% on RMSE, and
4.91% on MAPE.