Neuro-Fuzzy Forecasting of Tourist Arrivals

490 Pages·2015·3.49 MB·English

by  Hubert Fernando

Neuro-Fuzzy Forecasting of Tourist Arrivals

Doctor of Philosophy Thesis

Hubert Preman Fernando

Volume I

School of Applied Economics
Faculty of Business and Law
Victoria University
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Abstract

This study develops a model to forecast inbound tourism to Japan, using a
combination of artificial neural networks and fuzzy logic and compares the
performance of this forecasting model with forecasts from other quantitative
forecasting methods namely, the multi-layer perceptron neural network model, the
error correction model, the basic structural model, the autoregressive integrated
moving average model and the naïve model.

Japan was chosen as the country of study mainly due to the availability of reliable
tourism data, and also because it is a popular travel destination for both business and
pleasure. Visitor arrivals from the 10 most popular tourist source countries to Japan,
and total arrivals from all countries were used to incorporate a fairly wide variety of
data patterns in the testing process.

This research has established that neuro-fuzzy models can be used effectively in
tourism forecasting, having made adequate comparisons with other time series and
econometric models using real data. This research takes tourism forecasting a major
leap forward to an entirely new approach in time series pedagogy. As previous
tourism studies have not used hybrid combinations of neural and fuzzy logic in
tourism forecasting this research has only touched the surface of a field that has
immense potential not only in tourism forecasting but also in financial time series
analysis, market research and business analysis.
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Declaration

“I, Hubert Preman Fernando, declare that the PhD thesis entitled Neuro-Fuzzy
Forecasting of Tourist Arrivals is no more than 100,000 words in length, exclusive of
tables, figures, appendices, references and footnotes. This thesis contains no material
that has been submitted previously, in whole or in part, for the award of any other
academic degree or diploma. Except where otherwise indicated, this thesis is my own
work”.

Signature: Date: 2 November 2005
iv
Acknowledgements

I would like to express my sincere thanks to Professor Lindsay Turner, for his
academic and intellectual guidance, advice, assistance, encouragement and support
during the course of this research study, and the preparation of this thesis. I consider
myself very fortunate to have had Professor Turner as my principal supervisor not
only because of his excellent supervision but also because I was able to draw on his
wide knowledge and expertise in tourism economics and forecasting techniques.

I would also like to thank Dr. Leon Reznik, for introducing me to fuzzy logic, Dr.
Nada Kulendran for his advice on the application of the Error Correction Model and
Miss. Angelina Veysi and Miss Linda Osti for their assistance with data processing.

I would also like to thank my wife Kumarie, and two daughters Nikoli and Sohani for
their understanding, encouragement, support and immense patience during this period,
when they were in fact, deprived of my time and attention.

I dedicate this thesis to my late parents Hubert and Etta and my only sibling the late
Ramyani, who were also deprived of my time and attention during the final months of
their lives.
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Contents

Page
Abstract ii
Declaration iii
Acknowledgements iv
Volume I
Chapter 1 Introduction 1
1.1 Overview of the Thesis 3
1.2 The Research Problem 4
1.3 Aims and Objectives 8
1.4 Research Methodology 9
1.5 Data Content and Sources 17
1.6 Tourism in Japan 19
1.7 Visitor Arrivals to Japan 22
1.8 Japan's Economy 28
1.9 Japan's International Trade 33
Chapter 2 Literature Review 38
2.1 Introduction 38
2.2 Univariate Time Series Models 41
2.3 Econometric Models 45
2.4 Artificial Neural Networks (ANNs) 49
2.5 Fuzzy Logic 57
2.6 Neuro-Fuzzy Models 61
2.7 Neuro-Fuzzy modeling of Time Series 66
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Chapter 3 Neural Network Multi-layer Perceptron Models 67
3.1 Introduction 67
3.2 The Multi-Layer Perceptron Model 68
3.3 The Naïve Model 75
3.4 MLP Non-Periodic Forecasts 76
3.5 MPL Differenced Non-Periodic Forecast 83
3.6 MLP Partial Periodic Forecast 90
3.7 MLP Differenced Partial Periodic Forecast 97
3.8 MLP Periodic Forecast 104
3.9 Naïve Forecasts 111
3.10 Differenced and Undifferenced Model Comparison 118
3.11 MLP Model Comparison with the Naïve 125
3.12 Conclusion 131
Chapter 4 ARIMA and BSM Forecasting 133
4.1 Introduction 133
4.2 The ARIMA Model 134
4.3 The Basic Structural Model 137
4.4 Results of ARIMA(1) Forecasts 139
4.5 Results of ARIMA(1)(12) Forecasts 156
4.6 Results of BSM Forecasts 173
4.7 Model Comparison: ARIMA(1) and ARIMA(1)(12) 184
4.8 Model Comparison: ARIMA, BSM and Naïve Models 189
4.9 Conclusion 196
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Chapter 5 ECM and Multivariate Neural Network Forecasting 198
5.1 Introduction 198
5.2 The Error Correction Model (ECM) 200
5.3 The Multivariate Multi-layer Perceptron (MMLP) Model 204
5.4 Results of ECM Forecasts 207
5.5 Results of Multivariate Multi-layer Perceptron (MMLP) 247
5.6 Model Comparison 254
5.7 Conclusion 260
Volume II
Chapter 6 Adaptive Neuro-Fuzzy Forecasting 261
6.1 Introduction 261
6.2 The ANFIS Model 262
6.3 The Multivariate ANFIS Model 266
6.4 Results of ANFIS Forecasts 267
6.5 Results of Multivariate ANFIS Forecasts 275
6.6 Univariate and Multivariate ANFIS Model Comparison 282
6.7 Conclusion 289
Chapter 7 Conclusion 291
7.1 Introduction 291
7.2 Comparison of all models with the Naïve model 294
7.3 Comparison of all models against each other 301
7.4 Summary of conclusions 311
7.5 Recommendations for future research 319
References 321
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Appendix I 353
Appendix II 453
List of Figures
Figure 1.1 Total Monthly Arrivals from all Countries to Japan,
1st difference and 1st & 12th difference 18
Figure 1.2 Total Visitor Arrivals to Japan from 1964 to 2004 23
Figure 1.3 Tourist, Business and Other Arrivals to Japan
from 1978 to 2003 24
Figure 1.4 Japan's Economic Growth Rates 29
Figure 1.5 Japan's International Trade from 1978 to 2003 37
Figure 2.1 Basic Structure of an Artificial Neural Network 51
Figure 2.2 MLP Neural Network for Univariate Forecasting 52
Figure 2.3 MLP Neural Network for Multivariate Forecasting 53
Figure 2.4 Membership Functions of a Tourist Arrival System 59
Figure 3.1 Connectionist MLP Model for Univariate Forecasting 69
Figure 5.1 Connectionist MLP Model for Multivariate Forecasting 204
Figure 6.1 Connectionist ANFIS Model 264
Figure 7.1 The total number of forecasts with MAPE lower than
in the naïve model 300
Figure 7.2 The number of paired model comparisons with a significantly
lower MAPE 309
Figure 7.3 The number of forecasts with MAPE less than 10% 310
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List of Tables
Table 1.1 Data Structure 12
Table 1.2 Visitor Arrivals to Japan in 2000 by Gender and Age. 25
Table 1.3 Number of International Conventions and Participants 26
Table 1.4 Top 12 Countries of Visitor Origin from 1995 to 2003 27
Table 3.10.1 Univariate one month-ahead Forecasting Performance of
Differenced and Undifferenced Neural Network Models 121
Table 3.10.2 Univariate 12 months-ahead Forecasting Performance of
Differenced and Undifferenced Neural Network Models 122
Table 3.10.3 Univariate 24 months-ahead Forecasting Performance of
Differenced and Undifferenced Neural Network Models 123
Table 3.10.4 Forecasting Performance Comparison Summary of
Differenced and Undifferenced Neural Network Models 124
Table 3.11.1 Univariate one month-ahead Forecasting Performance of
Neural Network and Naïve Forecasts 128
Table 3.11.2 Univariate 12 months-ahead Forecasting Performance of
Neural Network and Naïve Forecasts 129
Table 3.11.3 Univariate 24 months-ahead Forecasting Performance of
Neural Network and Naïve Forecasts 130
Table 3.11.4 Forecasting Performance Comparison Summary of
Neural Network and Naïve Forecasts 131
Table 4.7.1 Univariate one month-ahead Forecasting Performance of
ARIMA(1) and ARIMA(1)(12) Models 186
Table 4.7.2 Univariate 12 months-ahead Forecasting Performance of
ARIMA(1) and ARIMA(1)(12) Models 187
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Table 4.7.3 Univariate 24 months-ahead Forecasting Performance of
ARIMA(1) and ARIMA(1)(12) Models 188
Table 4.7.4 Forecasting Performance Comparison Summary of
ARIMA(1) and ARIMA(1)(12) Models 189
Table 4.8.1 Univariate one month-ahead Forecasting Performance of
ARIMA and Basic Structural Models 193
Table 4.8.2 Univariate 12 months-ahead Forecasting Performance of
ARIMA and Basic Structural Models 194
Table 4.8.3 Univariate 24 months-ahead Forecasting Performance of
ARIMA and Basic Structural Models 195
Table 4.8.4 Forecasting Performance Comparison Summary of
ARIMA and Basic Structural Models 196
Table 5.6.1 Multivariate one month-ahead Forecasting Performance of
ECM and MMLP Models 256
Table 5.6.2 Multivariate 12 months-ahead Forecasting Performance of
ECM and MMLP Models 257
Table 5.6.3 Multivariate 24 months-ahead Forecasting Performance of
ECM and MMLP Models 258
Table 5.6.4 Forecasting Performance Comparison Summary of
Multivariate ECM and MMLP Models 259
Table 6.6.1 One month-ahead Forecasting Performance of
ANFIS and MLP Models 286
Table 6.6.2 12 months-ahead Forecasting Performance of ANFIS
and MLP Models 287