An adaptive autoregressive integration model for multi-variate time series analysis of extreme climate events
Year: 2026
Type: article
Abstract: 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.
Source: Scientific Reports
Authors: Ning Xin, Jionglong Su, Md Maruf Hasan
Institution: Xi’an Jiaotong-Liverpool University
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7154073936