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

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