UniTED: A Unified Time Series Event Detection Repository

• Janio Lima Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ) / Petróleo Brasileiro S.A. (Petrobras)
• Hélio Castro Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ)
• Luiz Oliveira Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ) / Petróleo Brasileiro S.A. (Petrobras)
• Ellen Paixão Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ)
• Lais Baroni Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ)
• Rebecca Salles INRIA
• Ricardo Vargas Petróleo Brasileiro S.A. (Petrobras)
• Eduardo Ogasawara Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ)

DOI: https://doi.org/10.5753/bresci.2025.247972

Resumo

Event detection in time series is essential for numerous real-world applications, from monitoring industrial systems to identifying health anomalies. Public annotated datasets are crucial for benchmarking, training, and validating detection models. Despite recent advances in the field, there is a lack of a standardized and unified repository for evaluating different event types, which limits progress in reproducibility, comparability, and model development. This paper presents the UniTED, a Unified Event Detection Dataset for time series. UniTED consolidates annotated series from diverse domains and offers a common format and protocol for evaluation. The repository supports three event types: anomalies, change points, and motifs. UniTED fosters reusability and reproducibility, contributing to improved performance assessment and model generalization across data analysis tasks. However, existing datasets have limitations, including poor standardization, a lack of annotation guidelines, limited support for different event types, and difficulties in automating performance evaluation. UniTED presents a harmonized ETL process, label and annotation conventions, and an open-source implementation. Three use cases are presented to demonstrate the applicability of the dataset.

Palavras-chave: Time Series Event Detection, Anomaly Detection, Change Point Detection, Motif Discovery

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