Journal Article

Time Series Classification with InceptionFCN

Ibrokhimov, Bunyodbek, Baydadaev, Shokhrukh, Kwon, Jangwoo, Usmankhujaev, Saidrasul

2021

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / Archives & records / Benchmarking / Classification / Computer vision / Data collection / Data Mining / Datasets / Deep learning / deep neural networks (DNN) / Dictionaries / fully convolutional network (FCN) / inception / Neural networks / Neural Networks, Computer / optimization / Teaching methods / Time Factors / Time series / time-series classification (TSC)