A technical analysis approach to tourism demand forecasting

被引:15
|
作者
Petropoulos, C
Nikolopoulos, K [1 ]
Patelis, A
Assimakopoulos, V
机构
[1] Univ Lancaster, Sch Management, Dept Management Sci, Lancaster Ctr Forecasting, Lancaster LA1 4YX, England
[2] Natl Tech Univ Athens, Sch Elect & Comp Engn, Forecasting Syst Unit, Zografou Athens 15773, Greece
[3] Minist Ecol & Finance, Informat Soc, Athens 10180, Greece
关键词
D O I
10.1080/13504850500065745
中图分类号
F [经济];
学科分类号
02 ;
摘要
Tourism demand forecasts are of great economic value both for the public and private sector. Any information concerning the future evolution of tourism flows, is of great importance to hoteliers, tour operators and other industries concerned with tourism or transportation, in order to adjust their policy and corporate finance. In the last few decades, numerous researchers have studied international tourism demand and a wide range of the available forecasting techniques have been tested. Major focus has been given to econometric studies that involve the use of least squares regression to estimate the quantitative relationship between tourism demand and its determinants. However, econometric models usually fail to outperform simple time series extrapolative models. This article introduces a new approach to tourism demand forecasting via incorporating technical analysis techniques. The proposed model is evaluated versus a range of classic univariate time series methods in terms of forecasting and directional accuracy.
引用
收藏
页码:327 / 333
页数:7
相关论文
共 50 条
  • [1] AN INTEGRATIVE APPROACH TO TOURISM DEMAND FORECASTING
    FAULKNER, B
    VALERIO, P
    [J]. TOURISM MANAGEMENT, 1995, 16 (01) : 29 - 37
  • [2] Forecasting tourism demand - An STM approach
    Greenidge, K
    [J]. ANNALS OF TOURISM RESEARCH, 2001, 28 (01) : 98 - 112
  • [3] A Hybrid Approach on Tourism Demand Forecasting
    Nor, M. E.
    Nurul, A. I. M.
    Rusiman, M. S.
    [J]. INTERNATIONAL SEMINAR ON MATHEMATICS AND PHYSICS IN SCIENCES AND TECHNOLOGY 2017 (ISMAP 2017), 2018, 995
  • [4] Bayesian BILSTM approach for tourism demand forecasting
    Kulshrestha, Anurag
    Krishnaswamy, Venkataraghavan
    Sharma, Mayank
    [J]. ANNALS OF TOURISM RESEARCH, 2020, 83
  • [5] Tourism demand forecasting: A deep learning approach
    Law, Rob
    Li, Gang
    Fong, Davis Ka Chio
    Han, Xin
    [J]. ANNALS OF TOURISM RESEARCH, 2019, 75 : 410 - 423
  • [6] Forecasting tourism demand: a cubic polynomial approach
    Chu, FL
    [J]. TOURISM MANAGEMENT, 2004, 25 (02) : 209 - 218
  • [7] TOURISM DEMAND: A NEURAL NETWORKS FORECASTING ANALYSIS
    Vortelinos, Dimitrios I.
    Gkillas, Konstantinos
    Floros, Christos
    Vasiliadis, Lavrentios
    [J]. BUSINESS MANAGEMENT THEORIES AND PRACTICES IN A DYNAMIC COMPETITIVE ENVIRONMENT, 2019, : 1899 - 1900
  • [8] Tourism Demand Forecasting: A Decomposed Deep Learning Approach
    Zhang, Yishuo
    Li, Gang
    Muskat, Birgit
    Law, Rob
    [J]. JOURNAL OF TRAVEL RESEARCH, 2021, 60 (05) : 981 - 997
  • [9] Tourism demand forecasting: An ensemble deep learning approach
    Sun, Shaolong
    Li, Yanzhao
    Guo, Ju-e
    Wang, Shouyang
    [J]. TOURISM ECONOMICS, 2022, 28 (08) : 2021 - 2049
  • [10] Analyzing and Forecasting Tourism Demand: A Rough Sets Approach
    Goh, Carey
    Law, Rob
    Mok, Henry M. K.
    [J]. JOURNAL OF TRAVEL RESEARCH, 2008, 46 (03) : 327 - 338