@Article{jqc.2020.07242,
AUTHOR = {Wangdong Jiang, Jie Wu, Guang Sun, Yuxin Ouyang, Jing Li, Shuang Zhou},
TITLE = {A Survey of Time Series Data Visualization Methods},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {105--117},
URL = {http://www.techscience.com/jqc/v2n2/40348},
ISSN = {2579-0145},
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.},
DOI = {10.32604/jqc.2020.07242}
}