文章基本信息

标题：Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review
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作者：Giuseppe Ciaburro ; Gino Iannace
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2021
卷号：6
期号：6
页码：55-84
DOI：10.3390/data6060055
出版社：MDPI Publishing
摘要：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.
关键词：time series data; machine learning; classification; regression; review time series data ; machine learning ; classification ; regression ; review