Detecting and locating patterns in time series using machine learning

A method is presented to detect and locate user-defined patterns in time series data. The method is based on decomposing time series into a sequence of fixed-length snapshots on which a classifier is applied. Snapshot classification results determine the exact position of the pattern. One advantage of this approach is that it can be applied to any process-specific pattern, e.g., spiking patterns, under- or overshoots, or (time-lagged) correlations. We demonstrate the efficacy of the approach by means of an example from steel production, namely a cold-rolling mill process. We detect two patterns: underswings and time-lagged spike repetition in multivariate series.