Visualization of time series statistical data by shape analysis (GDP ratio changes among Asia countries)

Yukari Shirota, Takako Hashimoto, Riri Fitri Sari

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


It has been very significant to visualize time series big data. In the paper we shall discuss a new analysis method called "statistical shape analysis" or "geometry driven statistics" on time series statistical data in economics. In the paper, we analyse the agriculture, value added and industry, value added (percentage of GDP) changes from 2000 to 2010 in Asia. We handle the data as a set of landmarks on a two-dimensional image to see the deformation using the principal components. The point of the analysis method is the principal components of the given formation which are eigenvectors of its bending energy matrix. The local deformation can be expressed as the set of non-Affine transformations. The transformations give us information about the local differences between in 2000 and in 2010. Because the non-Affine transformation can be decomposed into a set of partial warps, we present the partial warps visually. The statistical shape analysis is widely used in biology but, in economics, no application can be found. In the paper, we investigate its potential to analyse the economic data.

Original languageEnglish
Article number012013
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 5 Apr 2018
EventInternational Conference on Data and Information Science 2017, ICoDIS 2017 - Bandung, Indonesia
Duration: 5 Dec 20176 Dec 2017


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