ATSC-NEX: Automated Time Series Classification With Sequential Model-Based Optimization and Nested Cross-Validation

Mikko Tahkola (Corresponding Author), Guangrong Zou

Research output: Contribution to journalArticleScientificpeer-review

Abstract

New methods to perform time series classification arise frequently and multiple state-of-the-art approaches achieve high performance on benchmark datasets with respect to accuracy and computation time. However, often the modeling procedures do not include proper validation but rather rely only on either external test dataset or one-level cross-validation. ATSC-NEX is an automated procedure that employs sequential model-based optimization together with nested cross-validation to build an accurate and properly validated time series classification model. It aims to find an optimal pipeline configuration that includes the selection of input type and settings, as well as model type and hyperparameters. The results of a case study in which a model for the identification of diesel engine type is developed, show that the algorithm can efficiently find a well-performing pipeline configuration. The comparison between ATSC-NEX and some state-of-the-art methods on several benchmark datasets shows that similar accuracy can be achieved.
Original languageEnglish
Pages (from-to)39299-39312
Number of pages14
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 11 Apr 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Time series analysis
  • Time series classification
  • Data models
  • Computational modeling
  • Feature extraction
  • Benchmark testing
  • Transforms
  • Automated machine learning
  • Sequential model-based optimization
  • nested cross-validation

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