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Tourism forecasting using hybrid modified empirical mode decomposition and neural network

Yahya, Nurhaziyatul Adawiyah and Samsudin, Ruhaidah and Shabri, Ani (2017) Tourism forecasting using hybrid modified empirical mode decomposition and neural network. International Journal of Advances in Soft Computing and its Applications, 9 (1). pp. 14-31. ISSN 2074-8523

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Abstract

Due to the dynamically increasing importance of the tourism industry worldwide, new approaches for tourism demand forecasting are constantly being explored especially in this Big Data era. Hence, the challenge lies in predicting accurate and timely forecast using tourism arrival data to assist governments and policy makers to cater for upcoming tourists. In this study, a modified Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) model is proposed. This new approach utilized intrinsic mode functions (IMF) produced via EMD by reconstructing some IMFs through trial and error method, which is referred to in this research as decomposition. The decomposition and the remaining IMF components are then predicted respectively using ANN model. Lastly, the forecasted results of each component are aggregated to create an ensemble forecast for the tourism time series. The data applied in this experiment are monthly tourist arrivals from Singapore and Indonesia from the year 2000 to 2013 whereby the evaluations of the model’s performance are done using two wellknown measures; RMSE and MAPE. Based on the empirical results, the proposed model outperformed both the individual ANN and EMD-ANN models.

Item Type:Article
Additional Information:RADIS System Ref No:PB/2016/10392
Uncontrolled Keywords:neural network, time series
Subjects:Q Science
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
Science
ID Code:66467
Deposited By: Fazli Masari
Deposited On:03 Oct 2017 16:29
Last Modified:03 Oct 2017 16:29

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