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Dataset: Rare Event Classification in Multivariate Time Series

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arxiv 1809.10717 v4 pith:MABEYY4O submitted 2018-09-27 stat.ML cs.LG

Chitta Ranjan , Mahendranath Reddy , Markku Mustonen , Kamran Paynabar , Karim Pourak This is my paper · ORCID
classification stat.MLcs.LG
keywords dataeventdatasetmultivariaterareseriestimebuilding
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

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    Across five public datasets, weighted XGBoost and autoencoder baselines match or beat time series foundation models on anomaly detection and prediction, at a fraction of the computational cost.

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