Forecasting Philippine Household Final Consumption Expenditure on Education Using Discrete Wavelet Transformation on Hybrid ARIMA-ANN Model

Aarhus M Dela Cruz

doi:10.17485/ijst/2019/v12i33/146427

Abstract

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

Highlights

Education can be thought as one of the most important investment and gift that a parent or guardian can pass to its children
This study focused on determining best hybrid model based on Autoregressive Integrated Moving Average (ARIMA) and Artifical Neural Networks (ANN) for Household Final Consumption Expenditure (HFCE) on Education and forecasted several time-steps ahead
ARIMA-ANN is found accurate for Time Series (TS) forecasting

Summary

Introduction

Education can be thought as one of the most important investment and gift that a parent or guardian can pass to its children. Quality education is not just a school factor and through parents’ effort and support. A lot of parents strive hard to bring their child in private schools wherein they believe can offer higher quality of education. Most parents spend money on buying new school supplies for their child as well as different things like computers and different gadgets which may help their child learn. Several parents bring their child in tutorial centres or avails home tutoring services to elevate their children’s knowledge and competency. A novel hybridization of artificial neural networks and ARIMA models for time series forecasting.

Methods

Results

Conclusion