A review on distance based time series classification
Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN h...
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| Published in | Data mining and knowledge discovery Vol. 33; no. 2; pp. 378 - 412 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1384-5810 1573-756X 1573-756X |
| DOI | 10.1007/s10618-018-0596-4 |
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| Abstract | Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to its simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method. |
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| AbstractList | Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to its simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method. |
| Author | Mori, Usue Lozano, Jose A. Abanda, Amaia |
| Author_xml | – sequence: 1 givenname: Amaia orcidid: 0000-0001-8520-4456 surname: Abanda fullname: Abanda, Amaia email: aabanda@bcamath.org organization: Basque Center for Applied Mathematics (BCAM), Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU – sequence: 2 givenname: Usue surname: Mori fullname: Mori, Usue organization: Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Department of Applied Mathematics, Statistics and Operational Research, University of the Basque Country UPV/EHU – sequence: 3 givenname: Jose A. surname: Lozano fullname: Lozano, Jose A. organization: Basque Center for Applied Mathematics (BCAM), Intelligent Systems Group (ISG), Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU |
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| SubjectTerms | Academic Surveys and Tutorials Artificial Intelligence Chemistry and Earth Sciences Classification Classifiers Computer Science Data Mining and Knowledge Discovery Distance measurement Information Storage and Retrieval Kernel functions Measurement methods Physics Statistics for Engineering Taxonomy Time series |
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| Title | A review on distance based time series classification |
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