Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review
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 numeric...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Online Access: | https://www.mdpi.com/2306-5729/6/6/55 |
| _version_ | 1797532783096102912 |
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| author | Giuseppe Ciaburro Gino Iannace |
| author_facet | Giuseppe Ciaburro Gino Iannace |
| author_sort | Giuseppe Ciaburro |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:05:13Z |
| format | Article |
| id | doaj.art-3c202f4530ef43f6878bd8e5d14b38e9 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T11:05:13Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-3c202f4530ef43f6878bd8e5d14b38e92023-11-21T21:13:10ZengMDPI AGData2306-57292021-05-01665510.3390/data6060055Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A ReviewGiuseppe Ciaburro0Gino Iannace1Department of Architecture and Industrial Design, Università degli Studi della Campania, Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, ItalyDepartment of Architecture and Industrial Design, Università degli Studi della Campania, Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, ItalyTo 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.https://www.mdpi.com/2306-5729/6/6/55time series datamachine learningclassificationregressionreview |
| spellingShingle | Giuseppe Ciaburro Gino Iannace Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review Data time series data machine learning classification regression review |
| title | Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review |
| title_full | Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review |
| title_fullStr | Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review |
| title_full_unstemmed | Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review |
| title_short | Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review |
| title_sort | machine learning based algorithms to knowledge extraction from time series data a review |
| topic | time series data machine learning classification regression review |
| url | https://www.mdpi.com/2306-5729/6/6/55 |
| work_keys_str_mv | AT giuseppeciaburro machinelearningbasedalgorithmstoknowledgeextractionfromtimeseriesdataareview AT ginoiannace machinelearningbasedalgorithmstoknowledgeextractionfromtimeseriesdataareview |