Tourism Demand Forecasting Using Nonadditive Forecast Combinations

Abstract

Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.

Keywords

forecasting forecast combination tourism demand fuzzy integral grey theory

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References

Andrawis

R. R.

Atiya

A. F.

El-Shishiny

(2011). Combination of long-term and short-term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 27(3), 870-886. https://doi.org/10.1016/j.ijforecast.2010.05.019

Cang

(2014). A comparative analysis of three types of tourism demand forecasting models: Individual, linear combination and non-linear combination, International Journal of Tourism Research, 16(6), 596-607. https://doi.org/10.1002/jtr.1953

Chen

C. F.

Lai

M. C.

Yeh

C. C.

(2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26(February), 281-287. https://doi.org/10.1016/j.knosys.2011.09.002

Chen

C. I.

(2008). Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate. Chaos, Solitons & Fractals, 37(1), 278-287. https://doi.org/10.1016/j.chaos.2006.08.024

Chen

C. I.

Chen

H. L.

Chen

S. P.

(2008). Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear grey Bernoulli model NGBM(1,1). Communications in Nonlinear Science and Numerical Simulations, 13(6), 1194-1204. https://doi.org/10.1016/j.cnsns.2006.08.008

Chen

C. Y.

Huang

J. J.

(2019). Forming a hierarchical Choquet integral with a GA-based heuristic least square method. Mathematics, 7(12), 1155. https://doi.org/10.3390/math7121155

Chen

K. Y.

(2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems With Applications, 38(8), 10368-10376. https://doi.org/10.1016/j.eswa.2011.02.049

Chen

Y. Y.

Liu

H. T.

Hsieh

H. L.

(2019). Time series interval forecast using GM(1,1) and NGBM(1,1) models. Soft Computing, 23(5), 1541-1555. https://doi.org/10.1007/s00500-017-2876-0

China National Knowledge Infrastructure. (2021). China economic and social data platform. http://Data.Oversea.Cnki.Net/Chn

10.

Claveria

Monte

Torra

(2015). Tourism demand forecasting with neural network models: Different ways of treating information. International Journal of Tourism Research, 17(5), 492-500. https://doi.org/10.1002/jtr.2016

11.

Clemen

R. T.

(1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559-583. https://doi.org/10.1016/0169-2070(89)90012-5

12.

Clements

Hendry

(2009). Forecasting economic time series. Cambridge University Press.

13.

Coshall

J. T.

Charlesworth

(2011). A management orientated approach to combination forecasting of tourism demand. Tourism Management, 32(4), 759-769. https://doi.org/10.1016/j.tourman.2010.06.011

14.

DeMicco

Cetron

Davies

Guzman

(2021). COVID-19 impact on the future of hospitality and travel. Journal of Hospitality & Tourism Research, 45(5), 911-914. https://doi.org/10.1177/10963480211000822

15.

Demšar

(2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(1), 1-30. https://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf

16.

Divino

J. A.

McAleer

(2010). Modelling and forecasting daily international mass tourism to Peru. Tourism Management, 31(6), 846-854. https://doi.org/10.1016/j.tourman.2009.09.002

17.

Goodfellow

Bengio

Courville

(2016). Deep learning. MIT Press.

18.

Harvey

Leybourne

Newbold

(1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281-291. https://doi.org/10.1016/S0169-2070(96)00719-4

19.

Heaton

(2015). Artificial intelligence for humans: Deep learning and neural networks. St. Heaton Research.

20.

Qiu

R. T. R.

D. C.

Song

(2021). Hierarchical pattern recognition for tourism demand forecasting. Tourism Management, 84(June), 104263. https://doi.org/10.1016/j.tourman.2020.104263

21.

Y. C.

(2014). Non-additive similarity-based single-layer perceptron for multi-criteria collaborative filtering. Neurocomputing, 129(April), 306-314. https://doi.org/10.1016/j.neucom.2013.09.027

22.

Y. C.

(2020). A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems. Soft Computing, 24(6), 4259-4268. https://doi.org/10.1007/s00500-019-04191-0

23.

Y. C.

(2021). Forecasting tourism demand using fractional grey prediction models with Fourier series. Annals of Operations Research, 300(2), 467-491. https://doi.org/10.1007/s10479-020-03670-0

24.

Y. C.

Jiang

Lee

P. C

. (2019). Forecasting tourism demand by incorporating neural networks into grey-Markov models. Journal of the Operational Research Society, 70(1), 12-20. https://doi.org/10.1080/01605682.2017.1418150

25.

Huang

Y. L.

Lee

Y. H.

(2011). Accurately forecasting model for the stochastic volatility data in tourism demand. Modern Economy, 2(5), 823-829. https://doi.org/10.4236/me.2011.25091

26.

Ishibuchi

Nakashima

Nii

(2004). Classification and modeling with linguistic information granules: Advanced approaches to linguistic data mining. Springer-Verlag.

27.

Jiang

Wang

W. B.

Y. C.

Chiu

Y. J.

Tsao

S. R.

(2020). Pattern classification using tolerance rough sets based on non-additive grey relational analysis and DEMATEL. Grey Systems: Theory and Application, 11(1), 166-182. https://doi.org/10.1108/GS-05-2020-0062

28.

Kuncheva

L. I.

(2000). Fuzzy classifier design. Physica-Verlag. https://doi.org/10.1007/978-3-7908-1850-5

29.

Law

Fong

D. K. C.

Han

(2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75(March), 410-423. https://doi.org/10.1016/j.annals.2019.01.014

30.

Zhou

Liu

(2019). The combination of interval forecasts in tourism. Annals of Tourism Research, 75(March), 363-378. https://doi.org/10.1016/j.annals.2019.01.010

31.

(2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83(July), 102912. https://doi.org/10.1016/j.annals.2020.102912

32.

Liao

Tang

Al-Barakati

Llopis-Albert

(2020). A Choquet integral-based hesitant fuzzy gained and lost dominance score method for multi-criteria group decision making considering the risk preferences of experts: Case study of higher business education evaluation. Information Fusion, 62(October), 121-133. https://doi.org/10.1016/j.inffus.2020.05.003

33.

Lin

C. J.

Chen

H. F.

Lee

T. S.

(2011). Forecasting tourism demand using time series, artificial neural networks and multivariate adaptive regression splines: Evidence from Taiwan. International Journal of Business Administration, 2(2), 14-24. https://doi.org/10.5430/ijba.v2n2p14

34.

Liou

J. J. H.

(2013). New concepts and trends of MCDM for tomorrow: In honor of Professor Gwo-Hshiung Tzeng on the occasion of his 70th birthday. Technological and Economic Development of Economy, 19(2), 367-375. https://doi.org/10.3846/20294913.2013.811037

35.

Liou

J. J. H.

Chuang

Y. C.

Tzeng

G. H.

(2014). A fuzzy integral-based model for supplier evaluation and improvement. Information Sciences, 266(May), 199-217. https://doi.org/10.1016/j.ins.2013.09.025

36.

Liou

J. J. H.

Tzeng

G. H.

(2012). Comments on “Multiple criteria decision making (MCDM) methods in economics: An overview.” Technological and Economic Development of Economy, 18(4), 672-695. https://doi.org/10.3846/20294913.2012.753489

37.

Liu

Wang

(2020). A study on the influencing factors of tourism demand from Mainland China to Hong Kong. Journal of Hospitality & Tourism Research, 45(1), 171-191. https://doi.org/10.1177/1096348020944435

38.

Liu

Lin

(2010). Grey information: Theory and practical applications. Springer.

39.

Liu

Xie

(2013). Grey system theory and its applications. Scientific Press.

40.

Liu

Yang

Forrest

. (2017). Grey data analysis: Methods, models and applications. Springer. https://doi.org/10.1007/978-981-10-1841-1

41.

Liu

Peng

Bai

Zhu

Liao

(2014). Tourism flows prediction based on an improved grey GM(1,1) model. Procedia: Social and Behavioral Sciences, 138(July), 767-775. https://doi.org/10.1016/j.sbspro.2014.07.256

42.

Liu

Wang

(2019). Application of a novel nonlinear multivariate grey Bernoulli model to predict the tourist income of China. Journal of Computational and Applied Mathematics, 347(February), 84-94. https://doi.org/10.1016/j.cam.2018.07.044

43.

Murofushi

Sugeno

(1993). Some quantities represented by the Choquet integral. Fuzzy Sets and Systems, 56(2), 229-235. https://doi.org/10.1016/0165-0114(93)90148-B

44.

C. O.

Morzuch

B. J.

(2005). Evaluating time-series models to forecast the demand for tourism in Singapore: Comparing within-sample and post sample results. Journal of Travel Research, 43(4), 404-413. https://doi.org/10.1177/0047287505274653

45.

Onafowora

O. A.

Owoye

(2012). Modelling international tourism demand for the Caribbean. Tourism Economics, 18(1), 159-180. https://doi.org/10.5367/te.2012.0102

46.

Onisawa

Sugeno

Nishiwaki

M. Y.

Kawai

Harima

(1986). Fuzzy measure analysis of public attitude towards the use of nuclear energy. Fuzzy Sets and Systems, 20(3), 259-289. https://doi.org/10.1016/S0165-0114(86)90040-0

47.

Osyczka

(2002). Evolutionary algorithms for single and multicriteria design optimization. Physica-Verlag.

48.

Peng

K. H.

Tzeng

G. H

. (2013). A hybrid dynamic MADM model for problems-improvement in economics and business. Technological and Economic Development of Economy, 19(4), 638-660. https://doi.org/10.3846/20294913.2013.837114

49.

Qiu

R. T. R.

D. C.

Dropsy

Petit

Pratt

Ohe

(2021). Visitor arrivals forecasts amid COVID-19: A perspective from the Asia and Pacific team. Annals of Tourism Research, 88(May), 103155. https://doi.org/10.1016/j.annals.2021.103155

50.

Shen

S. J.

Song

(2008). An assessment of combining tourism demand forecasts over different time horizons. Journal of Travel Research, 47(2), 197-207. https://doi.org/10.1177/0047287508321199

51.

Shen

S. J.

Song

(2011). Combination forecasts of international tourism demand. Annals of Tourism Research, 38(1), 72-89. https://doi.org/10.1016/j.annals.2010.05.003

52.

Song

(2008). Tourism demand modelling and forecasting: A review of recent research. Tourism Management, 29(2), 203-220. https://doi.org/10.1016/j.tourman.2007.07.016

53.

Song

Qiu

R. T. R.

Park

(2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75(March), 338-362. https://doi.org/10.1016/j.annals.2018.12.001

54.

Song

Witt

S. F.

Wong

K. F.

D. C.

(2009). An empirical study of forecast combination in tourism. Journal of Hospitality & Tourism Research, 33(1), 3-29. https://doi.org/10.1177/1096348008321366

55.

Suganthi

Samuel

A. A.

(2012). Energy models for demand forecasting: A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. https://doi.org/10.1016/j.rser.2011.08.014

56.

Sugeno

Narukawa

Murofushi

(1998). Choquet integral and fuzzy measures on locally compact space. Fuzzy Sets and Systems, 99(2), 205-211. https://doi.org/10.1016/S0165-0114(97)00028-6

57.

Sun

Wang

Gao

(2016). Using a grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China. Tourism Management, 52(February), 369-379. https://doi.org/10.1016/j.tourman.2015.07.005

58.

Taiwan Tourism Bureau. (2021). Tourism Statistics Database of the Taiwan Tourism Bureau: Inbound visitors. https://stat.taiwan.net.tw/inboundSearch.

59.

Torraleja

F. A. G.

Vazquez

A. M.

Franco

M. J. B.

(2009). Flows into tourist areas: An econometric approach. International Journal of Tourism Research, 11(1), 1-15. https://doi.org/10.1002/jtr.657

60.

Tsui

W. H. K.

Balli

H. O.

Gilbey

Gow

(2014). Forecasting of Hong Kong airport’s passenger throughput. Tourism Management, 42(June), 62-76. https://doi.org/10.1016/j.tourman.2013.10.008

61.

Tzeng

G. H.

Huang

J. J.

(2011). Multiple attribute decision making: Methods and applications. CRC Press. https://doi.org/10.1201/b11032

62.

Tzeng

G. H.

Shen

K. Y.

(2017). New concepts and trends of hybrid multiple criteria decision making. CRC Press. https://doi.org/10.1201/9781315166650

63.

Vandeput

. (2021). Data science for supply chain forecasting. De Gruyter.

64.

Wang

C. H.

(2004). Predicting tourism demand using fuzzy time series and hybrid grey theory. Tourism Management, 25(3), 367-374. https://doi.org/10.1016/S0261-5177(03)00132-8

65.

Wang

Luo

Tang

(2018). Modeling a combined forecast algorithm based on sequence patterns and near characteristics: An application for tourism demand forecasting. Chaos, Solitons & Fractals, 108(March), 136-147. https://doi.org/10.1016/j.chaos.2018.01.028

66.

Wang

X. Z.

Y. L.

Dong

L. C.

Zhao

H. Y.

(2011). Particle swarm optimization for determining fuzzy measures from data. Information Sciences, 181(19), 4230-4252. https://doi.org/10.1016/j.ins.2011.06.002

67.

Wang

Klir

G. J.

(1992). Fuzzy measure theory. Plenum Press. https://doi.org/10.1007/978-1-4757-5303-5

68.

Wei

Xie

(2021). Parameter estimation for grey system models: A nonlinear least squares perspective. Communications in Nonlinear Science and Numerical Simulation, 95(April), 105653. https://doi.org/10.1016/j.cnsns.2020.105653

69.

Wong

K. K. F.

Song

Witt

S. F.

D. C.

(2007). Tourism forecasting: To combine or not to combine? Tourism Management, 28(4), 1068-1078. https://doi.org/10.1016/j.tourman.2006.08.003

70.

World Travel and Tourism Council. (2020). The economic impact of travel of travel and tourism. Author.

71.

D. C.

Cao

Wen

Song

(2020). Scenario forecasting for global tourism. Journal of Hospitality & Tourism Research, 45(1), 28-51. https://doi.org/10.1177/1096348020919990

72.

D. C.

Tso

K. F. G.

(2020). Forecasting tourist daily arrivals with a hybrid SARIMA–LSTM approach. Journal of Hospitality & Tourism Research, 45(1), 52-67. https://doi.org/10.1177/1096348020934046

73.

D. C.

Song

Shen

(2017). New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management, 29(1), 507-529. https://doi.org/10.1108/IJCHM-05-2015-0249

74.

Gao

Xiao

Yang

Chen

(2018). Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China. Energy, 157(August), 327-335. https://doi.org/10.1016/j.energy.2018.05.147

75.

Liu

Fang

(2015). Properties of the GM(1,1) with fractional order accumulation. Applied Mathematics and Computation, 252(February), 287-293. https://doi.org/10.1016/j.amc.2014.12.014

76.

Liu

Yao

Yan

(2013). The effect of sample size on the grey system model. Applied Mathematical Modelling, 37(9), 6577-6583. https://doi.org/10.1016/j.apm.2013.01.018

77.

Xie

(2020). A novel hybrid multivariate nonlinear grey model for forecasting the traffic-related emissions. Applied Mathematical Modelling, 77(January), 1242-1254. https://doi.org/10.1016/j.apm.2019.09.013

78.

Dang

Gong

(2017). Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China. Energy, 118(January), 473-480. https://doi.org/10.1016/j.energy.2016.10.003

79.

Yao

Cao

(2020). A neural network enhanced hidden Markov model for tourism demand forecasting. Applied Soft Computing, 94(September), 106465. https://doi.org/10.1016/j.asoc.2020.106465

80.

Zeng

Meng

(2015). Research on fractional accumulating generation operators and grey prediction models. Scientific Press.

81.

Zhang

Muskat

Law

(2020). Tourism demand forecasting: A decomposed deep learning approach. Journal of Travel Research, 60(5), 981-997. https://doi.org/10.1177/0047287520919522

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