Power transformer demand forecast with Box Jenkins ARIMA model
Özet
Demand forecasting is based on the principle of trying to forecast the demand for the outputs of
enterprises in the field of manufacturing or service for the next periods. It requires the estimation
of various future scenarios, if necessary, taking measures and taking steps, and during the
application phase, the technique that is most suitable for the characteristics of the examined data
set is selected and used. As a result of a healthy analysis carried out in this way, detailed plans
and strict measures can be taken for the unknown, negative scenarios of the future.
This study analyzes the characteristics of a series of power transformers of a company operating
in the electromechanical industry in the past years, and as a result of this analysis, the Box
Jenkins Autoregressive Integrated Moving Average method (ARIMA), which best fits the
results, is expected to occur for power transformers in the future. It was made to estimate the
amount of demand.
Within the scope of this study, firstly, the most suitable model was tried to be determined by
taking into consideration the past 132 months data of PTS. It was decided that the best choice
among the alternative models was the ARMA (4,4) x (0,1) 12 model. The model was found to be
stable and it was decided that the root mean square error (RMSE), mean absolute percentage
error (MAPE) and Theil inequality coefficient values determined in the performance
measurements were appropriate.
