Early Classification of Time Series Based on Reliable Labels

Early classification of time series aims to predict class labels as promptly as possible while maintaining sufficient accuracy. Existing approaches often rely on multiple classifiers for different input lengths, increasing training cost and complexity. We propose a unified framework comprising: 1) a subsequence classification model for variable-length inputs; 2) a subsequence information enough model; 3) a reliable label module; and 4) a confidence estimation mechanism. Our method dynamically determines when sufficient information is available for reliable classification. Experiments on 45 UCR datasets demonstrate superior performance over baseline methods, especially on SIMULATED, MOTION, and SENSOR tasks.