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
收藏
页数：9
相关论文
共 50 条
  • [41] Using machine learning to improve the hard modeling of NMR time series
    Hellwig, Jan
    Strauss, Tobias
    von Harbou, Erik
    Neymeyr, Klaus
    JOURNAL OF MAGNETIC RESONANCE, 2025, 370
  • [42] Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing
    Ty, Alexander J. A.
    Fang, Zheng
    Gonzalez, Rivver A.
    Rozdeba, Paul J.
    Abarbanel, Henry D., I
    NEURAL COMPUTATION, 2019, 31 (10) : 2004 - 2024
  • [43] Learning states and rules for detecting anomalies in time series
    Salvador, S
    Chan, P
    APPLIED INTELLIGENCE, 2005, 23 (03) : 241 - 255
  • [44] Learning States and Rules for Detecting Anomalies in Time Series
    Stan Salvador
    Philip Chan
    Applied Intelligence, 2005, 23 : 241 - 255
  • [45] Detecting Phishing Websites Using Machine Learning
    Alswailem, Amani
    Alabdullah, Bashayr
    Alrumayh, Norah
    Alsedrani, Aram
    2019 2ND INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS & INFORMATION SECURITY (ICCAIS), 2019,
  • [46] Detecting Variable Length Anomaly Patterns in Time Series Data
    Ngo Duy Khanh Vy
    Duong Tuan Anh
    DATA MINING AND BIG DATA, DMBD 2016, 2016, 9714 : 279 - 287
  • [47] Detecting Phone Theft Using Machine Learning
    Liu, Xinyu
    Wagner, David
    Egelman, Serge
    PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND SYSTEM (ICISS 2018), 2018, : 30 - 36
  • [48] Locating Objects in Warehouses Using BLE Beacons & Machine Learning
    Zadgaonkar, Hrushikesh
    Chandak, Manoj
    IEEE ACCESS, 2021, 9 : 153116 - 153125
  • [49] Detecting A Twitter Cyberbullying Using Machine Learning
    Dalvi, Rahul Ramesh
    Chavan, Sudhanshu Baliram
    Halbe, Apama
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS 2020), 2020, : 297 - 301
  • [50] Variable Length Methods for Detecting Anomaly Patterns in Time Series
    Leng, Mingwei
    Chen, Xiaoyun
    Li, Longjie
    PROCEEDINGS OF THE 2008 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 2, 2008, : 52 - +
12345