Open AccessVisualization of Multivariate Time Series pollutant variables in Malaysia
Author(s) -
Ulya Abdul Rahim,
Nurulkamal Masseran
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1988/1/012089
Subject(s) - visualization , multivariate statistics , multivariable calculus , smoothing , data mining , computer science , data visualization , time series , series (stratigraphy) , exploratory data analysis , multivariate analysis , process (computing) , statistics , mathematics , machine learning , engineering , computer vision , paleontology , control engineering , biology , operating system
Visualization and exploratory analysis is a crucial preliminary part of any data analysis process. Several visualization approaches have been introduced to evaluate the behaviors of time-dependent data. However, the visualization technique tends to be challenging when the data are high-dimensional and voluminous. Environmental data such as pollutant variables are often collected in multi-variables form and over time, resulting in a form of multivariate time-series data. To deal with this issue, this study provides several graphical approaches and methods which include the plots of multiple individually on a time-series, correlation matrix visualization and smoothing multivariate time-series. A case study involving data on air-pollution variables in Klang, Malaysia have been analyzed. The results found the all the methods able to provide an informative visualization on the behavior of multivariable time series of pollutant data.