Summer droughts are becoming increasingly severe under climate change, posing significant threats to global food security and ecosystem stability. While multivariate time series (MTS) analysis has emerged as a powerful tool for environmental modeling, it suffers from two limitations: (1) failure to account for temporal volatility patterns, and (2) difficulty in capturing non-stationary relationships among meteorological variables. Therefore, we introduce an innovative goal-oriented adaptive autoregressive integration system, i.e., Multi-Variate Time Series Former (MVformer) by integrating three modules: (1) an Adaptive Sampling Autoregressive Prediction (ASAP) module that dynamically balances teacher forcing and autoregression; (2) a volatility neural network capturing nonlinear temporal dependencies; and (3) extreme clustering for automated pattern discovery. MVformer first processes MTS through ASAP using causal attention and sliding windows for enhanced long-term forecasting, then fuses predictions with historical data into high-dimensional features for density-based extremal clustering to detect droughts. We validate MVformer based on meteorological data from 2,415 Chinese monitoring stations. Experiments show MVformer achieves optimal prediction accuracy (MSE: 0.617, MAE: 0.402, MAPE: 21.945%) and clustering quality (Inertia: 0.004, Silhouette Score: 0.424, Calinski-Harabasz: 767.442, Dunn index: 0.072). In summary, this study provides a robust predictive model for climate monitoring, drought early warning, and agricultural risk management.
Xin et al. (Mon,) studied this question.