Context Embedding Similarity based Semi-Supervised Active Learning for Time Series

Time series data, a crucial form of structured data across various domains, often necessitates a substantial quantity of labels for supervised learning. The complexity of annotating time series data, however, results in a high annotation cost. To address this challenge, active learning emerges as a strategic approach, selecting key samples and interacting with experts for data annotation. Its primary objective is to acquire high-quality training data at the lowest possible cost. This paper proposes a novel Semi-supervised ACTive learning (SACT) framework tailored for time series data. Initially, we establish a criterion to assess the importance of unlabeled samples, considering the unique characteristics of time series. We employ context embedding similarity scores to segment the time series data, prioritizing samples with high scores for labeling. Furthermore, to enhance the utilization of a single query, we propagate labels to adjacent data points. By experimenting on different combinations of query strategies and label propagation methods, our approach consistently outperforms other semi-supervised active learning frameworks for time series data in most scenarios. This superior performance underscores the efficacy of our approach.