Sensors, Vol. 22, Pages 157: Time Series Classification with InceptionFCN

Sensors, Vol. 22, Pages 157: Time Series Classification with InceptionFCN Sensors doi: 10.3390/s22010157 Authors: Saidasul Usmankhujaev Bunyodbek Ibrokhimov Shokhrukh Baydadaev Jangwoo Kwon 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.
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research