Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion - Laboratoire d'Informatique et de Mathématiques Access content directly
Proceedings Year : 2023

Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion

Alexis Bondu
  • Function : Author
Vincent Lemaire
  • Function : Author
Aurélien Renault
  • Function : Author

Abstract

Time Series Classification (TSC) has received much attention in the past two decades and is still a crucial and challenging problem in data science and knowledge engineering. Indeed, along with the increasing availability of time series data, many TSC algorithms have been suggested by the research community in the literature. Besides state-of-the-art methods based on similarity measures, intervals, shapelets, dictionaries, deep learning methods or hybrid ensemble methods, several tools for extracting unsupervised informative summary statistics, aka features, from time series have been designed in the recent years. Originally designed for descriptive analysis and visualization of time series with informative and interpretable features, very few of these feature engineering tools have been benchmarked for TSC problems and compared with state-of-the-art TSC algorithms in terms of predictive performance. In this article, we aim at filling this gap and propose a simple TSC process to evaluate the potential predictive performance of the feature sets obtained with existing feature engineering tools. Thus, we present an empirical study of 11 feature engineering tools branched with 9 supervised classifiers over 112 time series data sets. The analysis of the results of more than 10000 learning experiments indicate that feature-based methods perform as accurately as current state-of-the-art TSC algorithms, and thus should rightfully be considered further in the TSC literature.
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Dates and versions

hal-04350371 , version 1 (18-12-2023)

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Alexis Bondu, Vincent Lemaire, Dominique Gay, Aurélien Renault. Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion. 2023 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical and Electronics Engineers (IEEE), 2023, ⟨10.1109/IJCNN54540.2023.10191074⟩. ⟨hal-04350371⟩
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