On time series classification with dictionary-based classifiers

Article Properties
  • DOI (url)
    10.3233/ida-184333
  • Publication Date
    2019/10/24
  • Journal
    Intelligent Data Analysis
  • Indian UGC (journal)
  • Refrences
    28
  • James Large University of East Anglia, Norwich Research Park, UK
  • Anthony Bagnall University of East Anglia, Norwich Research Park, UK
  • Simon Malinowski University of Rennes 1, IRISA, LETG-Rennes COSTEL, France
  • Romain Tavenard University of Rennes 1, IRISA, LETG-Rennes COSTEL, France
Cite
Large, James, et al. “On Time Series Classification With Dictionary-Based Classifiers”. Intelligent Data Analysis, vol. 23, no. 5, 2019, pp. 1073-89, https://doi.org/10.3233/ida-184333.
Large, J., Bagnall, A., Malinowski, S., & Tavenard, R. (2019). On time series classification with dictionary-based classifiers. Intelligent Data Analysis, 23(5), 1073-1089. https://doi.org/10.3233/ida-184333
Large, James, Anthony Bagnall, Simon Malinowski, and Romain Tavenard. “On Time Series Classification With Dictionary-Based Classifiers”. Intelligent Data Analysis 23, no. 5 (2019): 1073-89. https://doi.org/10.3233/ida-184333.
Large J, Bagnall A, Malinowski S, Tavenard R. On time series classification with dictionary-based classifiers. Intelligent Data Analysis. 2019;23(5):1073-89.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Refrences
Title Journal Journal Categories Citations Publication Date
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances Data Mining and Knowledge Discovery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 511 2017
Time series representation and similarity based on local autopatterns Data Mining and Knowledge Discovery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 82 2016
Scalable time series classification Data Mining and Knowledge Discovery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 56 2016
Using dynamic time warping distances as features for improved time series classification Data Mining and Knowledge Discovery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 149 2016
The BOSS is concerned with time series classification in the presence of noise Data Mining and Knowledge Discovery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 182 2015
Refrences Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science7
Technology: Mechanical engineering and machinery6
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics3
Technology: Technology (General): Industrial engineering. Management engineering: Information technology1
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication1
Science: Science (General): Cybernetics: Information theory1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 7 is the most frequently represented among the references in this article. It primarily includes studies from Journal of Machine Learning Research and Information Sciences. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year