Heat Map Scope Technique for Stacked Time-series Data Visualization

2012 ◽  
Author(s):  
Yasuhiro Hashimoto ◽  
Ryo Matsushita
Keyword(s):  
Time Series ◽  
Data Visualization ◽  
Time Series Data ◽  
Series Data ◽  
Heat Map

scholarly journals Automation of Longitudinal Time Series Data Visualization using Plot Specifications obtained from XL file

2019 ◽  
Vol 8 (6) ◽  
pp. 3105-3109
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Data Visualization ◽  
Time Series Data ◽  
R Package ◽  
Series Data ◽  
Complex Data ◽  
Business Decision ◽  
Business Requirement ◽  
Visualization Techniques

Longitudinal Time Series data visualization plays important role in all sector of business decision making [9]. With enormous amount of complex data [11] from cloud and business requirement, number of graphs needed for decision making increased many folds. Generating enormous number of plots manually with more human input is tedious, time consuming and error prone. To avoid these issues, suitable visualization techniques with solid design principles become very important. We conceptualized and designed a novel method for automation of these processes. R-GGPLOT2[7] package and XL specifications file were primarily used to achieve this goal. We here show as how we can create multiple plots from time series data, plots specifications-XL file and R package GGPLOT2[7] in a single run. Since all required information are entered in XL sheet, R function can be run with no modification. Multiple plots can be generated by using enormous data available in production and service sectors such as finance, healthcare, transportation and food industries etc.

scholarly journals A Survey of Time Series Data Visualization Methods

2020 ◽  
Vol 2 (2) ◽  
pp. 105-117
Author(s):  
Wangdong Jiang ◽  
Jie Wu ◽  
Guang Sun ◽  
Yuxin Ouyang ◽  
Jing Li ◽  
...  
Keyword(s):  
Time Series ◽  
Data Visualization ◽  
Time Series Data ◽  
Series Data

scholarly journals spiralize: an R Package for Visualizing Data on Spirals

2021 ◽  
Author(s):  
Zuguang Gu ◽  
Daniel Huebschmann
Keyword(s):  
Time Series ◽  
General Solution ◽  
Data Visualization ◽  
Real World ◽  
Time Series Data ◽  
R Package ◽  
Series Data ◽  
Periodic Patterns ◽  
Real World Datasets ◽  
High Level

Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here we present the R package spiralize that provides a general solution for visualizing data on spirals. spiralize implements numerous graphics functions so that self-defined high-level graphics can be easily implemented by users. The power of spiralize is demonstrated by five real world datasets.

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