TOURISM DEMAND FORECASTING USING ARIMA MODELKaradzic, VesnaPejovic, BojanTransformations in Business & Economics
To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an ...
Then, the existing tourism demand forecasting studies usually collect the longest possible time series of tourism demand and match it to the same length of the search volume series to ensure that the forecasting model is fully developed. The implicit assumption of these studies is that the ...
Archer , B. H. 1980 . Forecasting demand: Quantitative and intuitive techniques. .International Journal of Tourism Management, 1 (1) : 5 – 12 . Google Scholar Archer , B. H. 1987 . “ Demand forecasting and estimation. ” . InTravel, tourism and hospitality research, Edited by: Ritchie...
Pektaş AO, Kerem Cigizoglu H (2013) ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. J Hydrol (Amsterdam) 500:21–36. https://doi.org/10.1016/j.jhydrol.2013.07.020 Article Google Scholar Witt SF, Witt CA (1995) Forecasting tourism demand: a review of empirical...
Both methods are com- monly used in forecasting. However, they are univariate models and lack explanatory power on how tourist demand is affected by various economic fac- tors. One contribution of this article is to refine the traditional univariate ARIMA into a causal model by adopting cross-...
We mainly use the ARIMA model for time-series analysis, which is constituted by using two popular models, Auto-regression (AR) and Moving Average (MA) models. The approaches which we used in our study are discussed below. 3.3.1. Machine learning algorithms for predicting preferred tour spots...
to be the best performing model among all the competing models examined. Keywords: forecasting performance; vector autoregressive process; over parameterization; Bayesian approach 2 1. Introduction A large number of published studies have appeared in the tourism forecasting ...
This integration addresses several identified gaps in tourism demand forecasting. Firstly, the hybrid model achieves higher predictive accuracy compared to standalone algorithms. Secondly, it demonstrates faster convergence, thereby reducing computational time and resources. Thirdly, by balancing FOX's ...
How can ANN-based demand modeling improve the accuracy of forecasting and decision-making in the tourism supply chain? 3. Problem description The main goal of the presented model is to analyze the decisions and interactions among tour operators, local operators, and service providers in the tourism...