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Related Experiment Videos

CTMNet: causal trend evolution and adaptive modulation for time series forecasting.

Yihao Wang1, Xiao Chen2,3, Jing Chen4

  • 1College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.

Scientific Reports
|May 8, 2026
PubMed
Summary

CTMNet introduces a novel coupled decomposition for multivariate time series forecasting. This network effectively models trend and residual components, improving prediction accuracy and robustness.

Keywords:
Adaptive modulationCausal trend evolutionDifferentiated modelingMultivariate time series forecastingSeries decomposition

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Time Series Analysis
  • Deep Learning Architectures

Background:

  • Multivariate time series forecasting commonly uses series decomposition to handle non-stationarity by separating trend and residual components.
  • Existing methods often assume component independence, ignoring how macro-trends influence micro-fluctuations, and face trade-offs in trend modeling efficiency vs. expressiveness.
  • Global attention methods in trend modeling can introduce noise and disrupt continuity, while linear methods lack dynamic capabilities.

Purpose of the Study:

  • To propose a novel coupled decomposition architecture, the Causal Trend Evolution and Adaptive Modulation Network (CTMNet), to address limitations in current time series forecasting models.
  • To develop a modeling strategy that balances the differentiation and interaction between trend and residual components.
  • To improve the accuracy and robustness of multivariate time series forecasting.

Main Methods:

  • Introduced the Causal Trend Encoder (CTE) using patch-level causal convolution and gating for accurate, linear-complexity trend modeling with causal inductive bias.
  • Developed the Adaptive Trend Modulation Interaction (ATMI) mechanism, leveraging CTE outputs to dynamically calibrate residual features, restoring trend-residual coupling.
  • Evaluated CTMNet on 10 real-world benchmark datasets for both long- and short-term forecasting tasks.

Main Results:

  • CTMNet demonstrated competitive or leading performance across 10 benchmark datasets.
  • The proposed architecture achieved superior prediction accuracy and robustness compared to 7 state-of-the-art models.
  • The coupled decomposition effectively models the interplay between trend and residual components.

Conclusions:

  • CTMNet offers an effective solution for multivariate time series forecasting by overcoming the component independence assumption.
  • The network's causal trend modeling and adaptive residual calibration enhance forecasting performance and reliability.
  • The findings highlight the benefits of coupled decomposition for capturing complex dynamics in time series data.