Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Giuseppe Ciaburro and Gino Iannace
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Giuseppe Ciaburro: Department of Architecture and Industrial Design, Università degli Studi della Campania, Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, Italy
Gino Iannace: Department of Architecture and Industrial Design, Università degli Studi della Campania, Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, Italy

Data, 2021, vol. 6, issue 6, 1-30

Abstract: To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data.

Keywords: time series data; machine learning; classification; regression; review (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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