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All Journal African Multidisciplinary Journal of Sciences and Artificial Intelligence
Emwinloghosa Kenneth Guobadia
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Materials Science & Nanotechnology

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Effect of Box-Cox Transformation on a k-th Weighted Moving Average Processes for Time Series Emwinloghosa Kenneth Guobadia; Kenneth Kevin Uadiale
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 1 No 1 (2024): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v1i1.3755

Abstract

In this paper, we examine, if the effect of transformation leads to improvement of model performance in time series modeling. The class of transformations that was considered is the Box-Cox family of transformation on the k-th weighted moving average (k-th WMA) model and autoregressive integrated moving average (ARIMA) model from a given nonstationary economic realization time series data. A real nonstationary economic time series data was used to demonstrate this procedure. The nonstationary time series data can be transformed to stationary data using the process of differencing alongside with Box-Cox transformation. The ARIMA model is fitted to the transformed data using the techniques of Box-Jenkins, where the best ARIMA is selected among the competing ARIMA models using Akaike information corrected criterian (AICc) while the best k-th WMA is selected among the competing models using some eval_uation metrics such as root mean square error (RMSE) and mean absolute error (MAE). Finally, the optimal model is selected between ARIMA model and k-th WMA using the RMSE and MAE. Our findings are that the transformed k-th WMA models outperformed the classical ARIMA models for the set of Box-Cox transformation parameters considered for the data used.
Effect of Box-Cox Transformation on a k-th Exponential Weighted Moving Average Processes for Time Series Kenneth Kevin Uadiale; Emwinloghosa Kenneth Guobadia
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 1 No 2 (2024): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v1i2.4167

Abstract

In the actual world, many time series are not stationary. The purpose of this research is to use the Box and Cox family of transformations to convert a nonstationary time series to a stationary time series in order to determine the influence of a transformation on the data. This is accomplished by setting particular values for the transformation parameter. The sample autocorrelation function (SACF) and the sample partial autocorrelation function (SPACF) were used to test for stationarity of the Box and Cox parameters. The ARIMA model is fitted to the transformed data using the techniques of Box-Jenkins, where the best ARIMA is selected among the competing ARIMA models using Akaike information corrected criterian (AICc) while the best k-th EWMA is selected among the competing models using some evaluation metrics such as root mean square error (RMSE) and mean absolute error (MAE). Finally, the optimal model is selected between ARIMA model and k-th EWMA using the RMSE and MAE. Our findings are that the transformed k-th EWMA models outperformed the classical ARIMA on the set of given data.
Co-Authors Kenneth Kevin Uadiale