title: Characteristics based dissimilarity measure for time series
metadata.name: dc.contributor.author
metadata.value: Staroverova, Kseniya Yurievna
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir Mansurovich
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2017-05-18T12:25:35Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2017-05-18T12:25:35Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2017-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Staroverova K. Yu., Bure V. M. Characteristics based dissimilarity measure for time series. Vestnik of Saint Petersburg University. Applied mathematics. Computer science. Control processes, 2017, volume 13, issue 1, pp. 51–60.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu10.2017.105
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/6352
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: It is necessary to invent dissimilarity measures which take into account the temporal nature of a
  time series. Such measures can be utilized for classification and clustering of time series. Great
  work has been conducted on this problem, but most measures use dimensionality reduction
  techniques. Such methods give accurate results for big data, but demonstrate a weakness now
  in short time series clustering. Many fields such as economics, demography, sociology, and
  others are presented by short time series. That is why a new method based on time series
  characteristics is introduced here. It is based on time series characteristics which are split into
  three groups: constant, dynamic and behavioural. A researcher can control the influence of the
  characteristics of each group as a result. Besides, we present a brief description of up-to-date
  dissimilarity measures from the R environment. The results of experiments on two synthetic
  data sets and comparison of our measure and other up-to-date methods are then presented.
  Refs 12. Figs 2. Table 1.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 13; Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: clustering
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: time series similarity measure
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: classification
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Characteristics based dissimilarity measure for time series
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 05-Staroverova.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/6352/1/05-Staroverova.pdf
resources.format: Adobe PDF
resources.size: 281,04 kB