Presentation is loading. Please wait.

Presentation is loading. Please wait.

Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system Haiyan Song School of Hotel and Tourism Management The.

Published byJulia Griffin Modified over 8 years ago

Similar presentations

Presentation on theme: "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system Haiyan Song School of Hotel and Tourism Management The."— Presentation transcript:

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 1

2 2

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

4 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)

5 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

6 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

7 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

8 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.

9 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

10 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 10 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.

11 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 11 3. Methodology - Data and Variables

12 12 SARS in 2003q2 Swine flu in 2009Q2 Models

13 13 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 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

14 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 - Forecasting evaluation

15 3. Methodology 3. Methodology TDFS New features added to the TDFS originally developed by Song et al. (2008): www.tourismforecasting.netwww.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

16 16

17 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

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

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

20 3. Methodology Baseline statistical forecasts: ADLM 20 Diagnostic statistics

21 3. Methodology 3. Methodology ─ Quantitative forecasting module 21

22 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

23 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.

24 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(2 nd ) 24 4. A Case Study

25 Evaluation of forecast accuracy (MAPE & RMSPE) 25 Country MAPE (%)RMSPE (%) SFGF1GF2SFGF1GF2 China 16.1911.9111.2917.6413.7813.03 Taiwan 10.748.809.0212.2010.0210.26 Japan 8.457.917.8711.6110.4910.65 Australia 2.943.304.884.644.265.29 U.K. 7.5811.5410.478.9512.4711.55 U.S. 7.254.694.397.414.934.64 Mean Short-haul 11.799.549.3913.8211.4311.31 Mean Long-haul 5.936.516.587.007.227.16 Mean Total 8.868.027.9910.419.339.24 Error reduction (%) GF1-SFGF2-SFGF2-GF1GF1-SFGF2-SFGF1-GF2 China –4.28–4.90–0.62–3.86–4.61–0.75 Taiwan –1.94–1.720.22–2.19–1.940.24 Japan –0.54–0.58–0.04–1.12–0.960.16 Australia 0.351.941.59–0.380.661.03 U.K. 3.952.88–1.073.522.59–0.92 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 0.580.650.070.220.16–0.06 Mean–0.84–0.87–0.04–1.08–1.17–0.09

26 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 = 0.091 Paired t-test: t (12) = –1.418, p = 0.091

27 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 = 0.054 Paired t-test: t (12) = –1.737, p = 0.054

28 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

29 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 30 Q & A

Download ppt "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system Haiyan Song School of Hotel and Tourism Management The."

Similar presentations

Agenda of Week V. Forecasting

Agenda of Week V. Forecasting
Slides 13a: Introduction; Qualitative Models MGS3100 Chapter 13 Forecasting.

Slides 13a: Introduction; Qualitative Models MGS3100 Chapter 13 Forecasting.
Assessing Uncertainty when Predicting Extreme Flood Processes.

Assessing Uncertainty when Predicting Extreme Flood Processes.
Forecasting OPS 370.

Forecasting OPS 370.
Use of Business Tendency Survey Results for Forecasting Industry Production in Slovakia Use of Business Tendency Survey Results for Forecasting Industry.

Use of Business Tendency Survey Results for Forecasting Industry Production in Slovakia Use of Business Tendency Survey Results for Forecasting Industry.
BUSINESS AND FINANCIAL LITERACY FOR YOUNG ENTREPRENEURS: EVIDENCE FROM BOSNIA-HERZEGOVINA Miriam Bruhn and Bilal Zia (World Bank, DECFP)

BUSINESS AND FINANCIAL LITERACY FOR YOUNG ENTREPRENEURS: EVIDENCE FROM BOSNIA-HERZEGOVINA Miriam Bruhn and Bilal Zia (World Bank, DECFP)
What is Forecasting? A forecast is an estimate of what is likely to happen in the future. Forecasts are concerned with determining what the future will.

What is Forecasting? A forecast is an estimate of what is likely to happen in the future. Forecasts are concerned with determining what the future will.
New Zealand’s tourism data Peter Ellis Manager Tourism Research and Evaluation 4 July 2012.

New Zealand’s tourism data Peter Ellis Manager Tourism Research and Evaluation 4 July 2012.
Forecasting Demand for Services

Forecasting Demand for Services
Validation and Monitoring Measures of Accuracy Combining Forecasts Managing the Forecasting Process Monitoring & Control.

Validation and Monitoring Measures of Accuracy Combining Forecasts Managing the Forecasting Process Monitoring & Control.
Forecasting Ross L. Fink.

Forecasting Ross L. Fink.
Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:

Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:
Data Sources The most sophisticated forecasting model will fail if it is applied to unreliable data Data should be reliable and accurate Data should be.

Data Sources The most sophisticated forecasting model will fail if it is applied to unreliable data Data should be reliable and accurate Data should be.
Forecasting IME 451, Lecture 2. Laws of Forecasting 1.Forecasts are always wrong! 2.Detailed forecasts are worse than aggregate forecasts! Dell forecasts.

Forecasting IME 451, Lecture 2. Laws of Forecasting 1.Forecasts are always wrong! 2.Detailed forecasts are worse than aggregate forecasts! Dell forecasts.
McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. C H A P T E R Market Potential and Sales Forecasting 6.

McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved. C H A P T E R Market Potential and Sales Forecasting 6.
Forecasting & Time Series Minggu 6. Learning Objectives Understand the three categories of forecasting techniques available. Become aware of the four.

Forecasting & Time Series Minggu 6. Learning Objectives Understand the three categories of forecasting techniques available. Become aware of the four.
08/08/02SJSU Bus 140 - David Bentley1 Course Part 2 Supply and Demand Management.

08/08/02SJSU Bus David Bentley1 Course Part 2 Supply and Demand Management.
The Strategic Role of Information in Sales Management

The Strategic Role of Information in Sales Management
Supply Chain Management (SCM) Forecasting 3

Supply Chain Management (SCM) Forecasting 3
Demand Forecasts The three principles of all forecasting techniques: –Forecasting is always wrong –Every forecast should include an estimate of error –The.

Demand Forecasts The three principles of all forecasting techniques: –Forecasting is always wrong –Every forecast should include an estimate of error –The.

Similar presentations

Ads by Google