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A novel hybrid ensemble learning paradigm for tourism forecasting

Shabri, Ani (2015) A novel hybrid ensemble learning paradigm for tourism forecasting. In: 2nd ISM International Statistical Conference 2014: Empowering the Applications of Statistical and Mathematical Sciences, ISM 2014, 12 August 2014 - 14 August 2014, Kuantan, Pahang, Malaysia.

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Official URL: http://dx.doi.org/10.1063/1.4907444

Abstract

In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:EMD, GMDH, tourism forecasting
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:59111
Deposited By: Haliza Zainal
Deposited On:18 Jan 2017 01:50
Last Modified:05 Aug 2021 02:32

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