Application of Time Series Analysis for Philippines’ Inflation Prediction
Allemar Jhone P. Delima1, Maria Tavita Q. Lumintac2
1Allemar Jhone P. Delima, College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, Philippines.
2Maria Tavita Q. Lumintac, College Teacher Education, Surigao State College of Technology, Surigao City, Philippines.

Manuscript received on 21 April 2019 | Revised Manuscript received on 26 May 2019 | Manuscript published on 30 May 2019 | PP: 1761-1765 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1852058119/19©BEIESP
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Abstract: This study established appropriate ARIMA(p,d,q) model in forecasting Philippines’ inflation rate for the years 2018 to 2022 using the univariate historical data of the country’s inflation rates from 1960-2017. In selecting the best model to be used in forecasting, the traditional assignment of p,d,q value using correlograms’ ACF and PACF plot and unit-root test data identification were observed and is selected according to the model with lowest AIC and forecast error statistical tools such as RMSE, MAE, and MAPE. Findings showed that ARIMA(1,0,0) is the best-fitted model when AIC is to observe. The inflation rate in the Philippines is forecasted to be at 7.05% by the end of 2018 whereas the highest predicted value is 8.93% in 2022. Using the forecast error criterion, ARIMA(7,0,0) was identified to be the best fit having a prediction of 4.60% inflation rate in 2018. Looking forward, a 5.21% inflation rate in the next twelve months is projected. The government may use the results of this research as input and or a guide to monetary policies and decisions that may help improve the Philippine economic status.
Index Terms: ARIMA, Data Mining, Forecasting, Inflation Prediction

Scope of the Article: Data Mining