Time Series Classification with InceptionFCN

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 propo...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-12, Vol.22 (1), p.157
Main Authors: Usmankhujaev, Saidrasul, Ibrokhimov, Bunyodbek, Baydadaev, Shokhrukh, Kwon, Jangwoo
Format: Article
Language:English
Subjects:
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)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22010157