Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 33, Pages: 1-10
Aarhus M. Dela Cruz1,2*
1City of Malolos Integrated School- Atlag, Schools Division of Malolos, City of Malolos, Bulacan, Philippines; [email protected]
2Polytechnic University of the Philippines, Sta. Mesa, Manila, Philippines
Objective: This study conveys a forecast of household final consumption expenditure in education of the Philippines from 1st quarter and 4th quarter of 2023. Methods/Analysis: The household final consumption expenditure in education data was obtained from the Philippine Statistics Authority which was part of the National Report for the 1st quarter of 1998 up to the 4th quarter of 2018. The data were forecasted using ARIMA, ANN, Hybrid ARIMA - ANN, and the proposed Discrete Wavelet Transformation using Daubechies filter on the Hybrid ARIMA - ANN. Findings: The forecasting accuracy of the model for a 1 year and 5 year forecast was compared with ARIMA, ANN, and Hybrid ARIMA - ANN through the value of its individual Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error. It was shown that the proposed DWT using Daubechies filter on Hybrid ARIMA-ANN has an MSE of 0.0009, RMSE of 0.0304, and MAPE of 0.1750 for 1 year forecast and an MSE of 0.0004, RMSE of 0.0194, and MAPE of 0.1167 for the 5 year forecast. It was revealed that the proposed model has the best forecasting performance comparing to ARIMA, ANN, and Hybrid ARIMA - ANN. Novelty/ Improvement: For the Department of Education of the Philippines, preparations and plans can be develop to cope up with the forecasted expenditure on education. For the citizen, the result of this research will give awareness on the movement of expenses in education and will let them prepare for it. Other forecasting models and filters on DWT can be utilized on future works which may improve the results of this study.
Keywords: Daubechies Filter, Discrete Wavelet Transformation, Education, Household Expenditure, Hybrid ARIMA-ANN, Time Series Forecasting
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