Study on the Test and Visualization of Change in Structures Associated with the Occurrence of Non-Stationary of Long-Term Time Series Data Based on Unit Root Test


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 7, pp. 289-302, Jul. 2019
https://doi.org/10.3745/KTSDE.2019.8.7.289, Full Text:
Keywords: Time Series, Non-Stationary, Unit Root Test, Visualization
Abstract

Structural change of time series means that the distribution of observations is relatively stable in the period of constituting the entire time series data, but shows a sudden change of the distribution characteristic at a specific time point. Within a non-stationary long-term time series, it is important to determine in a timely manner whether the change in short-term trends is transient or structurally changed. This is because it is necessary to always detect the change of the time series trend and to take appropriate measures to cope with the change. In this paper, we propose a method for decision makers to easily grasp the structural changes of time series by visualizing the test results based on the unit root test. Particularly, it is possible to grasp the short-term structural changes even in the long-term time series through the method of dividing the time series and testing it.


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Cite this article
[IEEE Style]
J. Yoo and J. Choo, "Study on the Test and Visualization of Change in Structures Associated with the Occurrence of Non-Stationary of Long-Term Time Series Data Based on Unit Root Test," KIPS Transactions on Software and Data Engineering, vol. 8, no. 7, pp. 289-302, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.7.289.

[ACM Style]
Jaeseong Yoo and Jaegul Choo. 2019. Study on the Test and Visualization of Change in Structures Associated with the Occurrence of Non-Stationary of Long-Term Time Series Data Based on Unit Root Test. KIPS Transactions on Software and Data Engineering, 8, 7, (2019), 289-302. DOI: https://doi.org/10.3745/KTSDE.2019.8.7.289.