Time series classification with inceptionFCN

Saidasul Usmankhujaev, Bunyodbek Ibrokhimov, Shokhrukh Baydadaev, Jangwoo Kwon

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm‐based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well‐known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state‐of‐the‐art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time‐series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

Original languageEnglish
Article number157
JournalSensors
Volume22
Issue number1
DOIs
StatePublished - 1 Jan 2022

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

Keywords

  • Deep neural networks (DNN)
  • Fully convolutional network (FCN)
  • Inception
  • Optimization
  • Time‐series classification (TSC)

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