# Dataset: Rare Event Classification in Multivariate Time Series

arXiv: https://arxiv.org/abs/1809.10717 · HTML: https://mlsanity.com/paper/1809.10717/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey on Rare Event Prediction](https://mlsanity.com/paper/2309.11356/) (2023)
- [Time Series Foundational Models: Their Role in Anomaly Detection and Prediction](https://mlsanity.com/paper/2412.19286/) (2024)
- [Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing](https://mlsanity.com/paper/2407.01644/) (2024)
- [Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures](https://mlsanity.com/paper/2410.22594/) (2024)
- [Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production](https://mlsanity.com/paper/2311.09333/) (2023)
- [Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrieval](https://mlsanity.com/paper/2603.12544/) (2026)
- [The Risk Shadow of Principal Component Analysis: When 99.9999% Variance Preservation Causes Catastrophic Decision Errors](https://mlsanity.com/paper/2606.14533/) (2026)

## 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.