Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

Ale Ebrahim, S., Poshtan, J., Jamali, S. M., & Ale Ebrahim, N. (2020). Quantitative and Qualitative Analysis of Time- Series Classification using Deep Learning. IEEE Access, 8, 90202 - 90215

17 Pages Posted: 18 Mar 2021

See all articles by Saba Ale Ebrahim

Saba Ale Ebrahim

Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran

Javad Poshtan

Iran University of Science and Technolog

Seyedh Mahboobeh Jamali

Universiti Sains Malaysia (USM) - School of Educational Studies

Nader Ale Ebrahim

Centre for Research Services, Institute of Management and Research Services (IPPP), University of Malaya (UM); University of Malaya (UM) - Department of Engineering Design and Manufacture

Date Written: May 8, 2020

Abstract

Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords co-occurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details.

Keywords: Time-Series Classification, Deep Learning, Remote Sensing, Signal Processing, Bibliometrics, Research Productivity

JEL Classification: L11, L1, L2, M11, M12, M1, M54, Q1, O1, O3, P42, P24, P29, Q31, Q32, L17

Suggested Citation

Ale Ebrahim, Saba and Poshtan, Javad and Jamali, Seyedh Mahboobeh and Ale Ebrahim, Nader, Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning (May 8, 2020). Ale Ebrahim, S., Poshtan, J., Jamali, S. M., & Ale Ebrahim, N. (2020). Quantitative and Qualitative Analysis of Time- Series Classification using Deep Learning. IEEE Access, 8, 90202 - 90215, Available at SSRN: https://ssrn.com/abstract=3738282

Saba Ale Ebrahim

Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran ( email )

No.24, Agaahi Alley, Dabestan St.
Seyed-khandan, Resalat Highway, POB: 16315-989
Tehran, Tehran 16315-989
Iran

Javad Poshtan

Iran University of Science and Technolog ( email )

‎ Iran (Islamic Republic)
Narmak
Tehran
Iran

Seyedh Mahboobeh Jamali

Universiti Sains Malaysia (USM) - School of Educational Studies ( email )

Penang, 11800
Malaysia

Nader Ale Ebrahim (Contact Author)

Centre for Research Services, Institute of Management and Research Services (IPPP), University of Malaya (UM) ( email )

Kuala Lumpur, Wilayah Persekutuan 50603
University of Malaya (UM)
Kuala Lumpur, Wilayah Persekutuan 50603
Malaysia

HOME PAGE: http://https://umresearch.um.edu.my/

University of Malaya (UM) - Department of Engineering Design and Manufacture ( email )

Kuala Lumpur, 50603
Malaysia

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