  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.
