MET-C3: Mixed Exogenous–Temporal Dependency Modeling for Non-Stationary Time Series Forecasting

Nonstationary time series forecasting is challenging, as the dependency structure among historical observations, temporal lags, and exogenous variables varies over time. Existing models capture temporal patterns implicitly, but often lack an explicit mechanism for distinguishing temporal inertia from exogenous impacts. This paper proposes MET-C3, a mixed node dependency graph framework for graph guided nonstationary forecasting. MET-C3 represents lagged target variables and exogenous variables as a unified node set, estimates dependency matrices in rolling windows, and introduces autocorrelation constrained coupling to weight dependencies among lag nodes, between lag and exogenous nodes, and among exogenous nodes. It further applies TMFG sparse topology construction and LoGo partial correlation inference to extract compact structural features, which are integrated with multiple forecasting backbones. Experiments on a proprietary Shandong Electricity Market dataset and three public datasets from electricity, mobility, and finance show that MET-C3 consistently reduces forecasting error across backbones and domains. Robustness analyses demonstrate its advantages under stronger structural nonstationarity, while runtime comparisons show comparable computational cost to the original backbones. These results indicate that explicit mixed dependency modeling offers an effective and interpretable approach to nonstationary forecasting.