Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging.
Prevailing discrete-time architectures often impose rigid temporal alignment across heterogeneous variables, which may limit their ability to capture continuously evolving external effects.
To address this limitation, we introduce CTIA-ODE, a continuous-time forecasting framework centered on Continuous-Time Inter-Variable Attention (CTIA).
Specifically, CTIA is embedded within an ordinary differential equation (ODE) solver, enabling the continuously evolving endogenous trajectory to dynamically query a static exogenous context along the integration path without requiring explicit temporal alignment.
To improve the stability of the continuous latent dynamics, we further introduce a dual-stabilization strategy:
- Architecturally, a Linear Residual Augmentation (LRA) module provides a macroscopic trend anchor,
- while an Outlier-Robust Hybrid Loss (ORHL) mitigates the influence of heavy-tailed anomalies during optimization.
Extensive experiments across eight real-world datasets demonstrate that CTIA-ODE achieves state-of-the-art forecasting performance across diverse datasets and prediction horizons, while maintaining a favorable accuracy-efficiency trade-off with a compact memory footprint and low inference latency, particularly under long-horizon forecasting settings.
Anonymous code is available at https://anonymous.4open.science/r/CTIA-ODE-15A6/.