Mining Temporal Data

Robert Moskovitch ()
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Robert Moskovitch: Ben Gurion University, Software and Information Systems Engineering

A chapter in Machine Learning for Data Science Handbook, 2023, pp 469-490 from Springer

Abstract: Abstract The information technology revolution, especially with the adoption of the internet of things, longitudinal data in many domains becomes more available and accessible for secondary analysis. Such data provides meaningful opportunities to understand process in many domains along time but also challenges. A main challenge is the heterogeneity of the temporal variables due to the different types of data, whether a measurement or an event, and type of samplings: fixed or irregular. Other variables can be also events that may or not have duration. In this review, we discuss the various types of temporal data, and the various relevant analysis methods. Starting with fixed frequency variables, with forecasting and time series methods, and proceeding with sequential data, and sequential patterns mining, and time intervals mining for events having various time duration. Also the use of various deep learning based architectures for temporal data is discussed. The challenge of heterogeneous multivariate temporal data analysis and discuss various options to deal with it, focusing on an increasingly used option of transforming the data into symbolic time intervals through temporal abstraction and the use of time intervals related patterns discovery for temporal knowledge discovery, clustering, classification prediction and more. Finally, we discuss the overview of the field, and areas in which more contributions are needed.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_21

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DOI: 10.1007/978-3-031-24628-9_21

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