Improving Prediction Accuracy of Time Series Data Using ARIMA-ANN Hybrid Model and Regression Analysis

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นัท กุลวานิช

Abstract

The new hybrid model using linear regression methods to combine both ARIMA and Artificial Neural Networks (ARIMA-ANN-REG Hybrid Model) achieves better prediction accuracy than the traditional hybrid model combining ARIMA and ANN (ARIMA-ANN Hybrid Model). This research examines and compares the prediction accuracy among ARIMA model, ARIMA-ANN model, and ARIMA-ANN-REG model by using both real and simulated data for the comparison of prediction accuracy. The simulated data are generated from 8 ARIMA processes and the real data are comprised of six real datasets in Thailand. The results suggest that ARIMA-ANN-REG model has the highest prediction accuracy in both real and simulated data.

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วิทยาศาสตร์กายภาพ
Author Biography

นัท กุลวานิช, Chulalongkorn University

ภาควิชาสถิติ คณะพาณิชยศาสตร์และการบัญชี จุฬาลงกรณ์มหาวิทยาลัย แขวงวังใหม่ เขตปทุมวัน กรุงเทพมหานคร 10330

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