Dataset: Rare Event Classification in Multivariate Time Series
This provides a dataset for researchers and practitioners in manufacturing to address paper break prediction, but it is incremental as it only introduces data without new methods.
The authors tackled the problem of rare event classification in multivariate time series by providing a real-world dataset from the pulp-and-paper industry, which includes sensor readings and labels for paper break events, aimed at building early prediction models.
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.