Open Access
ARTICLE
A Survey of Time Series Data Visualization Methods
Wangdong Jiang1, Jie Wu1,*, Guang Sun1,2, Yuxin Ouyang3, Jing Li3, Shuang Zhou2
1 School of Information Management and Technology, Hunan University of Finance and Economics, Changsha, 410000, China
2 The University of Alabama, Tuscaloosa, Alabama, USA
3 Housheng School of International Education, Hunan University of Finance and Economics, Changsha, 410000, China
* Corresponding Author: Jie Wu. Email:
Journal of Quantum Computing 2020, 2(2), 105-117. https://doi.org/10.32604/jqc.2020.07242
Received 01 April 2020; Accepted 06 July 2020; Issue published 19 October 2020
Abstract
In the era of big data, the general public is more likely to access big
data, but they wouldn’t like to analyze the data. Therefore, the traditional data
visualization with certain professionalism is not easy to be accepted by the
general public living in the fast pace. Under this background, a new general
visualization method for dynamic time series data emerges as the times require.
Time series data visualization organizes abstract and hard-to-understand data
into a form that is easily understood by the public. This method integrates data
visualization into short videos, which is more in line with the way people get
information in modern fast-paced lifestyles. The modular approach also
facilitates public participation in production. This paper summarizes the dynamic
visualization methods of time series data ranking, studies the relevant literature,
shows its value and existing problems, and gives corresponding suggestions and
future research prospects.
Keywords
Cite This Article
W. Jiang, J. Wu, G. Sun, Y. Ouyang, J. Li
et al., "A survey of time series data visualization methods,"
Journal of Quantum Computing, vol. 2, no.2, pp. 105–117, 2020.