Combining statistical and judgmental forecasts via a web-

based tourism demand forecasting system

Haiyan Song

School of Hotel and Tourism ManagementThe Hong Kong Polytechnic University

Hong Kong

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2

Agenda

Introduction Literature Review Methodology A Case Study Conclusion Q & A

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Major challenges in tourism demand forecasting

High sensitivity to external shocks

High volatility

(seasonality)

Lack of data

4

1. Introduction

Complexity of touristbehavior

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.

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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)

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1. Introduction

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).

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2. Literature Review ─ User intervention in the forecasting process

− 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: 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 ─ 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.

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a. GF are more accurate than SFb. Delphi forecasts are more accurate than

forecasts from statistized group (GF2>>GF1)c. Experts with more domain knowledge

produce more accurate forecasts

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3. 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

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3. Methodology - Data and Variables

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12

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40,000

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120,000

160,000

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86 88 90 92 94 96 98 00 02 04 06 08 10

Tourist arrivals, Australia

0

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2,000,000

3,000,000

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86 88 90 92 94 96 98 00 02 04 06 08 10

Tourist arrivals, China

0

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400,000

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86 88 90 92 94 96 98 00 02 04 06 08 10

Tourist arrivals, Japan

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

86 88 90 92 94 96 98 00 02 04 06 08 10

Tourist arrivals, Taiwan

0

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120,000

160,000

200,000

86 88 90 92 94 96 98 00 02 04 06 08 10

Tourist arrivals, UK

0

100,000

200,000

300,000

400,000

86 88 90 92 94 96 98 00 02 04 06 08 10

Tourist arrivals, USA

SARS in 2003q2

Swine flu in 2009Q2

Models

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Events that need to be considered over the forecasting period:

(1) Japan Earthquake in 2011

(2) High-speed Railway (January 2010 - 2015)

(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, respectivelyExperts

• 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)

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3. Methodology - Forecasting evaluation

3. Methodology TDFSNew features added to the TDFS originally developed by

Song et al. (2008): www.tourismforecasting.net • 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

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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

InputOutput

Output

3. Methodology ─ Data module

18Screen shot of uploaded data

3. Methodology ─ Data module

19Screen shot of the data presentation

3. Methodology

• Baseline statistical forecasts: ADLM

20Diagnostic statistics

3. Methodology ─ Quantitative forecasting module

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3. Methodology ─ Judgmental forecasting module

• Scenario Analysis

• Statistical Adjustment

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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 ─ Judgmental forecasting module

• Scenario Analysis

• Statistical Adjustment

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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 2010Q1-2015Q4: China, Taiwan, Japan, the US, the UK, & Australia

– Two rounds: 16 (1st), 13(2nd)

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4. A Case Study

Evaluation of forecast accuracy (MAPE & RMSPE)

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CountryMAPE (%) RMSPE (%)

SF GF1 GF2 SF GF1 GF2China 16.19 11.91 11.29 17.64 13.78 13.03Taiwan 10.74 8.80 9.02 12.20 10.02 10.26Japan 8.45 7.91 7.87 11.61 10.49 10.65Australia 2.94 3.30 4.88 4.64 4.26 5.29U.K. 7.58 11.54 10.47 8.95 12.47 11.55U.S. 7.25 4.69 4.39 7.41 4.93 4.64Mean Short-haul 11.79 9.54 9.39 13.82 11.43 11.31Mean Long-haul 5.93 6.51 6.58 7.00 7.22 7.16Mean Total 8.86 8.02 7.99 10.41 9.33 9.24Errorreduction (%) GF1-SF GF2-SF GF2-GF1 GF1-SF GF2-SF GF1-GF2

China –4.28 –4.90 –0.62 –3.86 –4.61 –0.75Taiwan –1.94 –1.72 0.22 –2.19 –1.94 0.24Japan –0.54 –0.58 –0.04 –1.12 –0.96 0.16Australia 0.35 1.94 1.59 –0.38 0.66 1.03U.K. 3.95 2.88 –1.07 3.52 2.59 –0.92U.S. –2.57 –2.87 –0.30 –2.48 –2.77 –0.29Mean Short-haul –2.26 –2.40 –0.14 –2.39 –2.50 –0.12Mean Long-haul 0.58 0.65 0.07 0.22 0.16 –0.06Mean –0.84 –0.87 –0.04 –1.08 –1.17 –0.09

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11.7%

10.1%9.9%

9.2% 9.2% 9.5%

8.3%

7.7%

8.7%

7.8%

8.0%

8.9% 8.8%

10.0%

9.3% Mean_GF1: 9.06%

Mean_GF2: 8.83%

7%

8%

8%

9%

9%

10%

10%

11%

11%

12%

12%

1 2 3 4 5 6 7 8 9 10 11 12 13

MAPE_GF1 MAPE_GF2Mean_GF1 Mean_GF2

Evaluation of forecasting accuracy - Individual participants’ forecasting performances over rounds (MAPE)

Paired t-test:t (12) = –1.418, p = 0.091

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15.1%

12.2%11.95%

10.5%10.38%

12.1%

11.1% 11.1% 11.2%11.7%

12.0%

10.3%

9.4%

10.8%

9.8%10.1%

10.6%

11.0%

Mean_GF1: 11.30%

Mean_GF2: 10.85%

8%

9%

10%

11%

12%

13%

14%

15%

16%

1 2 3 4 5 6 7 8 9 10 11 12 13

RMSPE_GF1 RMSPE_GF2Mean_GF1 Mean_GF2

Evaluation of forecasting accuracy - Individual participants’ forecasting performances over rounds (RMSPE)

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.

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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

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Q & A

Thank you!