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Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system

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 . Agenda. Introduction Literature Review Methodology A Case Study Conclusion

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Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system

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  1. 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

  2. Agenda • Introduction • Literature Review • Methodology • A Case Study • Conclusion • Q & A

  3. 1. Introduction Complexity of tourist behavior Wide choice of forecast variables Frechtling (2001)

  4. 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. 1. Introduction 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. 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─User intervention in the forecasting process • The size of the adjustment, direction of the adjustment (+/-), & characteristics of the forecasting series affect forecasting performance. • 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. 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. 3. Methodology - Research hypothesis • GF are more accurate than SF • Delphi forecasts are more accurate than forecasts from statistized group (GF2>>GF1) • Experts with more domain knowledge produce more accurate forecasts 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.

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

  11. SARS in 2003q2 Swine flu in 2009Q2 Models

  12. 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, respectively Experts

  13. 3. Methodology - Forecasting evaluation • 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)

  14. 3. Methodology TDFS New 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

  15. Output Input Output Input 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).

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

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

  18. 3. Methodology • Baseline statistical forecasts: ADLM • Diagnostic statistics

  19. 3. Methodology─ Quantitative forecasting module

  20. 3. Methodology─ Judgmental forecasting module • Scenario Analysis • Statistical Adjustment 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

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

  22. 4. A Case Study • 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)

  23. Evaluation of forecast accuracy (MAPE & RMSPE)

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

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

  26. 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.

  27. 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

  28. Thank you! Q & A

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