Tourism forecasting using hybrid modified empirical mode decomposition and neural network

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

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

Two-Stage Model for Exchange Rate Forecasting by EMD and Random Forest

This study applied random forest (RF) and empirical mode decomposition (EMD) techniques to exchange rate forecasting. The aim of this study is to examine the feasibility of the proposed EMD-RF model in exchange rate forecasting. For this purpose, the original exchange rate series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs) and one residual component. Then, a random forest model is constructed to forecast these IMFs and residual value individually, and then all these forecasted values are aggregated to produce the final forecasted value for exchange rates. The daily USD/NTD, USD/JPY, USD/HKD and USD/AUD exchange rates were employed as the data set. The experimental results are that MAPE for the four data sets are, respectively, 0.278%, 1.143%, 0.153% and 5.944%, which shows good performance according to the 10% threshold suggested by Lewis

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.Peer ReviewedPostprint (author's final draft

Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level

A Study on Forecasting Models for Cruise Demand: Comparisons Between South Korea and Hong Kong

The cruise industry has emerged to be an important part of the tourism sector since there has been a large increase in the number of passengers worldwide. The purpose of this study is to forecast cruise tourism demand, as doing so can ensure better planning, efficient preparation at the destination port and act as a basis for the elaboration of future plans. In this study, forecasting methods such as Exponential Triple Smoothing (ETS), Autoregressive Integrated Moving Average (ARIMA) and Group Method of Data Handling (GMDH) are tested to estimate the cruise demand and the best forecasting model is suggested by comparing the forecast accuracy. The total number of foreign cruise passengers are used as the measure of cruise demand. The results show that GMDH outperforms ETS and ARIMA in terms of forecasting accuracy.Chapter 1: Introduction ..................................................... 1 1.1. Motivations and Objectives of Research ................ 1 1.2. Scope of Study ....................................................... 4 Chapter 2: Overview of Cruise Industry ........................... 5 2.1. History of Cruise .................................................... 5 2.2. Cruise Market ........................................................ 7 2.3. Cruise Tourism in South Korea ............................. 12 2.3.1. Cruise Tourism in Busan ................................... 17 2.4. Cruise Tourism in Hong Kong ............................... 20 2.5. Literature Review ................................................ 24 Chapter 3: Forecasting Models ....................................... 27 3.1. Autoregressive Integrated Moving Average (ARIMA) ........................................................................ 27 3.2. Exponential Triple Smoothing (ETS) ..................... 29 3.3. Group Method of Data Handling (GMDH) ............ 31 Chapter 4: Data Collection and Analysis ......................... 35 4.1. The Data .............................................................. 35 4.2. Analysis of Time Series Features of the Data ....... 37 4.3. Accuracy Measurement of Model ........................ 41 4.4. Experiment Results and Generating Future Forecasts....................................................................... 42 Chapter 5: Conclusions and Future Study ....................... 51 References ....................................................................... 53 Acknowledgments ........................................................... 57Maste