Dataset: Rare Event Classification in Multivariate Time Series
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Abstract (arXiv)
A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The primary purpose of the data is thought to be building a classification model for early prediction of the rare event. However, it can also be used for multivariate time series data exploration and building other supervised and unsupervised models.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2018.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 2.
7 papers.
- 2023A Comprehensive Survey on Rare Event Predictioncited by 18
- 2024Time Series Foundational Models: Their Role in Anomaly Detection and Predictioncited by 4
- 2024Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturingcited by 2
- 2024Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failurescited by 2
- 2023Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Productioncited by 0
- 2026Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrievalcited by 0
- 2026The Risk Shadow of Principal Component Analysis: When 99.9999% Variance Preservation Causes Catastrophic Decision Errorscited by 0
- 2026The Risk Shadow of Principal Component Analysis: When 99.9999% Variance Preservation Causes Catastrophic Decision Errors12 Jun 2026
- 2026Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrieval13 Mar 2026
- 2024Time Series Foundational Models: Their Role in Anomaly Detection and Prediction26 Dec 2024
- 2024Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures29 Oct 2024
- 2024Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing1 Jul 2024
- 2023Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production15 Nov 2023
- 2023A Comprehensive Survey on Rare Event Prediction20 Sep 2023