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
  • [31] Failure prediction using machine learning and time series in optical network
    Wang, Zhilong
    Zhang, Min
    Wang, Danshi
    Song, Chuang
    Liu, Min
    Li, Jin
    Lou, Liqi
    Liu, Zhuo
    OPTICS EXPRESS, 2017, 25 (16): : 18553 - 18565
  • [32] Time Series Data Prediction using IoT and Machine Learning Technique
    Kumar, Raghavendra
    Kumar, Pardeep
    Kumar, Yugal
    INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE, 2020, 167 : 373 - 381
  • [33] Multivariate Time Series Evapotranspiration Forecasting using Machine Learning Techniques
    Liyew, Chalachew Muluken
    Meo, Rosa
    Di Nardo, Elvira
    Ferraris, Stefano
    38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023, 2023, : 377 - 380
  • [34] Forecasting insect abundance using time series embedding and machine learning
    Palma, Gabriel R.
    Mello, Rodrigo F.
    Godoy, Wesley A. C.
    Engel, Eduardo
    Lau, Douglas
    Markham, Charles
    Moral, Rafael A.
    ECOLOGICAL INFORMATICS, 2025, 85
  • [35] Water Demand Forecasting Using Machine Learning and Time Series Algorithms
    Ibrahim, Tarek
    Omar, Yasser
    Maghraby, Fahima A.
    2020 INTERNATIONAL CONFERENCE ON EMERGING SMART COMPUTING AND INFORMATICS (ESCI), 2020, : 325 - 329
  • [36] Jump detection in financial time series using machine learning algorithms
    Jay F. K. Au Yeung
    Zi-kai Wei
    Kit Yan Chan
    Henry Y. K. Lau
    Ka-Fai Cedric Yiu
    Soft Computing, 2020, 24 : 1789 - 1801
  • [37] Simulation of rainfall and runoff time series using robust machine learning
    Alizadeh, Amir
    Rajabi, Ahmad
    Shabanlou, Saeid
    Yaghoubi, Behrouz
    Yosefvand, Fariborz
    IRRIGATION AND DRAINAGE, 2021, 70 (01) : 84 - 102
  • [38] Time-Series Data Analytics Using Spark and Machine Learning
    Thongtra, Patcharee
    Sapronova, Alla
    FOUNDATIONS OF INTELLIGENT SYSTEMS, ISMIS 2017, 2017, 10352 : 509 - 515
  • [39] Time series prediction using machine learning: a case of Bitcoin returns
    Shakri, Irfan Haider
    STUDIES IN ECONOMICS AND FINANCE, 2022, 39 (03) : 458 - 470
  • [40] A new algorithm for time series prediction using machine learning models
    Yeturu Jahnavi
    Poongothai Elango
    S. P. Raja
    Javier Parra Fuente
    Elena Verdú
    Evolutionary Intelligence, 2023, 16 : 1449 - 1460
12345