Multivariate temporal data analysis - a review

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1 Scopus citations

Abstract

The information technology revolution, especially with the adoption of the Internet of Things, longitudinal data in many domains become more available and accessible for secondary analysis. Such data provide 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 studies and contributions are needed. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining.

Original languageEnglish
Article numbere1430
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume12
Issue number1
DOIs
StatePublished - 1 Jan 2022

ASJC Scopus subject areas

  • Computer Science (all)

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