Analyzing a Large Number of Time Series
Learning and Monitoring Complex Patterns
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Beschreibung
The modern availability of multistream data in a wide range of applications is pushing the need to address time series data analysis in new and innovative ways. Sensor data in Industry 4.0 applications is one such example, and patient monitoring in emergency rooms is another. This context leads to challenges coming from unsynchronized data resolution, a variety of data structures, including imaging, and complex challenges in data integration.
This monograph will fill a gap in the methodology and practice of analyzing a large number of time series with complex temporal dependencies. It will focus on time series that have applications in disciplines such as business, climate, engineering, finance, health analytics, remote sensing, and manufacturing, providing novel and sophisticated data analytic techniques for researchers and practitioners. Specific topics that will be covered include state-of-the-art data science methods that enable systematic learning and monitoring of complex dependence patterns in a large number of time series; supervised and unsupervised learning (classification, clustering, biclustering) methods which provide monitoring and diagnostic capabilities to the analysis of time series; and monitoring and control of many time series in order to facilitate decision making.The modern availability of multistream data in a wide range of applications is pushing the need to address time series data analysis in new and innovative ways. Sensor data in Industry 4.0 applications is one such example, and patient monitoring in emergency rooms is another. This context leads to challenges coming from unsynchronized data resolution, a variety of data structures, including imaging, and complex challenges in data integration.
This monograph will fill a gap in the methodology and practice of analyzing a large number of time series with complex temporal dependencies. It will focus on time series that have applications in disciplines such as business, climate, engineering, finance, health analytics, remote sensing, and manufacturing, providing novel and sophisticated data analytic techniques for researchers and practitioners. Specific topics that will be covered include state-of-the-art data science methods that enable systematic learning and monitoring of complex dependence patterns in a large number of time series; supervised and unsupervised learning (classification, clustering, biclustering) methods which provide monitoring and diagnostic capabilities to the analysis of time series; and monitoring and control of many time series in order to facilitate decision making.Fills gap in the methodology and practice of analyzing a large number of time series with complex temporal dependencies Applications in business, climate, engineering, finance, health analytics, remote sensing, and manufacturing Topics that include state-of-the-art data science methods
Autor*in
Ron Kenett
Themen in »Analyzing a Large Number of Time Series«
Time Series Analysis Supervised Learning for Time Series ARIMA models Vector ARIMA models Multivariate Quality Control Industrial Process Monitoring Supervised Learning Unsupervised Learning