Detecting and locating patterns in time series using machine learning

被引：7

作者：

Janka, Dennis ^{[1
]}

Lenders, Felix ^{[1
]}

Wang, Shiyu ^{[1
,2
]}

Cohen, Andrew ^{[1
]}

Li, Nuo ^{[1
]}

机构：

[1] ABB AG, Corp Res Germany, Wallstadter Str 59, D-68526 Ladenburg, Germany

[2] Karlsruhe Inst Technol, D-76133 Karlsruhe, Germany

来源：

CONTROL ENGINEERING PRACTICE | 2019年 / 93卷

关键词：

Pattern recognition; Machine learning; Time-series classification; Industrial data analytics; Metals processing; Neural networks;

D O I：

10.1016/j.conengprac.2019.104169

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：9

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