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Enhancing the predictive performance of non-stationary time series data through various transformations
Journal of the Korean Data & Information Science Society 2024;35:145-52
Published online January 31, 2024;  https://doi.org/10.7465/jkdi.2024.35.1.145
© 2024 Korean Data and Information Science Society.

Young Eun Jeon1 · Suk-Bok Kang2 · Jung-In Seo3

12Department of Statistics, Yeungnam University
3Department of Information Statistics, Andong National University
Correspondence to: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5A8063350).
1 Graduate student, Department of Statistics, Yeungnam University, Gyeongsan 38541, Korea.
2 Professor, Department of Statistics, Yeungnam University, Gyeongsan 38541, Korea.
3 Corresponding author: Assistant professor, Department of Information Statistics, Andong National University, Andong 36729, Korea. E-mail: leehoo1928@gmail.com
Received November 14, 2023; Revised November 28, 2023; Accepted December 6, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Time series datasets observed in the real world frequently have trends and seasonality, and these datasets are called non-stationary time series datasets. For non-stationary time series datasets, a transformation is an indispensable task for enhancing the accuracy of analysis. A Box-Cox transformation is one of the most widely used transformation methods, but it has the burden of estimating a power parameter from the observed data. In response to this predicament, this paper provides a scaled logit transformation and substantiates its superiority in comparison with the Box-Cox transformation. A notable advantage of the provided transformation is that it has no stress associated with parameter estimation, unlike the Box-Cox transformation. For illustration purposes, a bike sharing dataset with features related to weather, temporal, and seasonality is used as a case study. The superiority and applicability of the provided transformation are meticulously examined by applying various modeling techniques including statistical and machine learning techniques to this dataset.
Keywords : Box-Cox transformation, machine learning, non-stationary time series, scaled logit transformation.