UTSC: Uncertainty-Aware Framework for Early Time Series Classification

Naixin Yan, Jun Hu, Yuqi Chu, Shuguo Hu, Huaiwen Zhang

Abstract: Early Time Series Classification (ETSC) is pivotal in time-sensitive real-world applications. However, existing methods face two significant challenges: (1) the lack of explicit representation of uncertainty, which leads to inaccurate decision-making in the early stages, and (2) the difficulty in handling heteroscedastic uncertainty, where uncertainty dynamically changes as more data is observed, especially in early stages of time series classification. Traditional ETSC methods assume that the predicted probability distributions at each time step are completely reliable and accurate. This assumption neglects the inherent uncertainty in the classification process, leading to cumulative errors that ultimately affect the overall accuracy and reliability of the classification decision. To address these challenges, we propose a novel Uncertainty-aware framework for early Time Series Classification (UTSC), designed to model and adapt to uncertainty during early classification dynamically. UTSC incorporates an Uncertainty Probability Decoder (UPD), which captures the inherent randomness and uncertainty in early-stage data by leveraging hidden-layer information to model variability adaptively. Additionally, UTSC employs an enhanced probability reweighting method to adjust the probability distributions dynamically, enabling the model to account for uncertainty and make more informed decisions. We evaluate UTSC on 45 diverse datasets, comparing its performance against eight baseline models. The results demonstrate that UTSC significantly outperforms existing methods. Our code is publicly available1.

External IDs:dblp:conf/ijcnn/YanHCHZ25