Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system Haiyan Song School of Hotel and Tourism Management The Hong Kong Polytechnic University Hong Kong 1

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Agenda Introduction Literature Review Methodology A Case Study Conclusion Q & A 3

Major challenges in tourism demand forecasting High sensitivity to external shocks High volatility (seasonality) Lack of data 4 1. Introduction Complexity of tourist behavior Complexity of tourist behavior Wide choice of forecast variables Frechtling (2001)

1. Introduction Successful tourism managers need to find ways of reducing the risk of future failures in tourism demand forecasting. There is no single quantitative model outperforms all others on all occasions (Song & Li 2008). Combining statistical forecasts with judgments may improve forecasting performance. 5

A follow-up study of Song, Witt & Zhang (2008) Aim of this study: – To further develop the web-based Tourism Demand Forecasting System (TDFS) by combining the statistical forecasts with judgmental forecasts generated by a panel of experts (postgraduate students and academic staff) 6 1. Introduction

2. Literature Review 2. Literature Review ─ Quantitative forecasting methods Univariate time-series methods: Naïve, moving average, exponential smoothing, Box-Jenkins models, etc.) Causal econometric approaches : ADLM, error correction model (ECM), vector autoregressive (VAR) model, time varying parameter (TVP) model, almost ideal demand system (AIDS) None of these models outperforms the others on all occasions (Song & Li, 2008). 7

2. Literature Review 2. Literature Review ─ User intervention in the forecasting process −Finding 1 −Finding 1: The user’s judgment in identifying characteristics of the series to be forecast and the appropriate data processing approach is beneficial for forecast error reduction. −Finding 2 −Finding 2: Judgmental adjustments improve forecasting accuracy when forecasters have important information about the outcome variable that is not available to the statistical model. −Finding 1 −Finding 1: The user’s judgment in identifying characteristics of the series to be forecast and the appropriate data processing approach is beneficial for forecast error reduction. −Finding 2 −Finding 2: Judgmental adjustments improve forecasting accuracy when forecasters have important information about the outcome variable that is not available to the statistical model. 8 The size of the adjustment, direction of the adjustment (+/-), & characteristics of the forecasting series affect forecasting performance.

2. Literature Review 2. Literature Review ─ Computer-based innovation Large forecast errors exist with the existing FSSs – Consist only of pure time-series methods ignoring the changes in outcome variables resulting from explanatory variables. – Most of them require users to have a strong mathematics/statistics background. However, tourism practitioners often lack such a background. – Do not provide suggestions or guidelines for users during the forecasting process. – No evaluation of forecasting performance is provided. 9

a. GF are more accurate than SF b. Delphi forecasts are more accurate than forecasts from statistized group (GF2>>GF1) c. Experts with more domain knowledge produce more accurate forecasts Methodology - Research hypothesis Note: GF: Judgmental adjustment of statistical forecasts, SF: statistical forecasts produced by ADLM models, GF1: Group forecasts in the first round of Delphi survey, GF2: Group forecasts in the second round of Delphi survey.

Quarterly tourist arrivals to Hong Kong:1985Q1-2010Q4 – 3 short-haul markets (China, Taiwan and Japan) – 3 long-haul markets (the USA, the UK and Australia) Model: Autoregressive Distributed Lag Model (ADLM) Data sources: (1) Hong Kong Tourism Board, (2) IMF Methodology - Data and Variables

12 SARS in 2003q2 Swine flu in 2009Q2 Models

13 Events that need to be considered over the forecasting period: (1) Japan Earthquake in 2011 (2) High-speed Railway (January ) (3) 2012 London Olympic Games (27 July to 12 August 2012) (4) Three New Themed Lands in the Hong Kong Disneyland to be introduced in 2011, 2012 and 2013, respectively Events that need to be considered over the forecasting period: (1) Japan Earthquake in 2011 (2) High-speed Railway (January ) (3) 2012 London Olympic Games (27 July to 12 August 2012) (4) Three New Themed Lands in the Hong Kong Disneyland to be introduced in 2011, 2012 and 2013, respectively Experts

Accuracy measures (2011Q2-2012Q1)  Absolute Percentage Error (APE)  Mean Absolute Percentage Error (MAPE)  Root Mean Square Percentage Error (RMPSE) Forecasting performance evaluation Comparison between GF and SF – R squared, MAPE, RMPSE Comparison between industry and academic groups – Independent sample t test, Mann-Whitney U test Comparison between rounds – One sample t-test, Wilcoxon signed ranks test Performance by individuals – One sample t-test, Wilcoxon signed ranks test (Round 1 vs. 2) Methodology - Forecasting evaluation

3. Methodology 3. Methodology TDFS New features added to the TDFS originally developed by Song et al. (2008): User-friendliness Modularity Flexibility Enhanced website administration system Java Server Pages (JSP) and R-based applications Implementation of open source R code Improvements in judgmental inputs 15

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3. Methodology 3. Methodology ─ TDFS Four types of tourism forecasts : tourist arrivals, tourist expenditure, demand for hotel rooms (i.e. High Tariff A and B hotel rooms, Medium Tariff hotel rooms, & Tourist Guesthouses), & expenditure by sector (i.e. hotels, shopping, meals, entertainment & tours). Input Output

3. Methodology 3. Methodology ─ Data module 18 Screen shot of uploaded data

3. Methodology 3. Methodology ─ Data module 19 Screen shot of the data presentation

3. Methodology Baseline statistical forecasts: ADLM 20 Diagnostic statistics

3. Methodology 3. Methodology ─ Quantitative forecasting module 21

3. Methodology 3. Methodology ─ Judgmental forecasting module Scenario Analysis Statistical Adjustment 22 It offers four baseline scenarios (5%/1% higher and lower than the baseline growth rates) plus a customized scenario where users can input their own estimates

3. Methodology 3. Methodology ─ Judgmental forecasting module Scenario Analysis Statistical Adjustment 23 It allows users to adjust the forecasts of both the dependent and independent variables.

The Dynamic Delphi Survey via TDFS – Participants: postgraduate students and staff from the School of Hotel and Tourism Management at The Hong Kong Polytechnic University – Arrival forecasts of six source markets over 2010Q Q4: China, Taiwan, Japan, the US, the UK, & Australia – Two rounds: 16 (1st), 13(2 nd ) A Case Study

Evaluation of forecast accuracy (MAPE & RMSPE) 25 Country MAPE (%)RMSPE (%) SFGF1GF2SFGF1GF2 China Taiwan Japan Australia U.K U.S Mean Short-haul Mean Long-haul Mean Total Error reduction (%) GF1-SFGF2-SFGF2-GF1GF1-SFGF2-SFGF1-GF2 China –4.28–4.90–0.62–3.86–4.61–0.75 Taiwan –1.94– –2.19– Japan –0.54–0.58–0.04–1.12– Australia – U.K – –0.92 U.S. –2.57–2.87–0.30–2.48–2.77–0.29 Mean Short-haul –2.26–2.40–0.14–2.39–2.50–0.12 Mean Long-haul –0.06 Mean–0.84–0.87–0.04–1.08–1.17–0.09

26 Evaluation of forecasting accuracy - Individual participants’ forecasting performances over rounds (MAPE) Evaluation of forecasting accuracy - Individual participants’ forecasting performances over rounds (MAPE) Paired t-test: t (12) = –1.418, p = Paired t-test: t (12) = –1.418, p = 0.091

27 Evaluation of forecasting accuracy - Individual participants’ forecasting performances over rounds (RMSPE) Evaluation of forecasting accuracy - Individual participants’ forecasting performances over rounds (RMSPE) Paired t-test: t (12) = –1.737, p = Paired t-test: t (12) = –1.737, p = 0.054

5.Conclusion Overall, the results showed that a greater forecast accuracy was achieved with the judgmentally adjusted statistical forecasts than with the statistical forecasts alone. The benefits of including judgmental inputs in quantitative forecasts depend on the characteristics of the data series being examined. 28

5.Conclusion Reasons for improved forecasting accuracy of TDFS: – Advanced econometric modelling method – TDFS provides flexible adjustment options – Use of a web-based platform – Participants have a high level of technical knowledge of tourism demand forecasting 29

30 Q & A