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.
@article{arxiv.1809.10717,
title = {Dataset: Rare Event Classification in Multivariate Time Series},
author = {Chitta Ranjan and Mahendranath Reddy and Markku Mustonen and Kamran Paynabar and Karim Pourak},
journal= {arXiv preprint arXiv:1809.10717},
year = {2019}
}