Statistical analysis and predictions of fuel price distributions in the Philippines
Abstract
We use interevent time statistics and Auto-Regressive Integrated Moving Average (ARIMA) modeling to analyze the diesel and unleaded prices of nine Philippine fuel companies. Results show that the prices typically change 6 days after a previous fuel price rollback or increase. Using ARIMA(1,1,1), the Jetti diesel price prediction has the best fit with respect to the original prices with Caltex as its covariate. For unleaded prices, Total has relatively the best fitting prediction, also with Caltex as the covariate. Evaluation of the goodness of predictions leads to the determination of influence factors on the fuel pricing of Philippine fuel companies.
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