Tourism forecasting: A review of methodological developments over the last decade

2018 ◽  
Vol 25 (3) ◽  
pp. 469-492 ◽  
Author(s):  
Eden Xiaoying Jiao ◽  
Jason Li Chen
Keyword(s):  
Time Series ◽  
Spatial Regression ◽  
Demand Forecasting ◽  
Time Series Models ◽  
Tourism Demand ◽  
Mixed Frequency ◽  
Forecasting Models ◽  
The Past ◽  
Forecasting Performance ◽  
Performance Evaluation And Comparison

This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

scholarly journals Tourism Demand Modelling and Forecasting: A Review of Literature

2021 ◽  
Vol 10(4) (10(4)) ◽  
pp. 1370-1393
Author(s):  
Musonera Abdou ◽  
Edouard Musabanganji ◽  
Herman Musahara
Keyword(s):  
Time Series ◽  
Spatial Regression ◽  
Demand Forecasting ◽  
Tourism Demand ◽  
New Developments ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Modelling And Forecasting ◽  
Forecasting Techniques

This research examines 145 key papers from 1979 to 2020 in order to gain a better sense of how tourism demand forecasting techniques have changed over time. The three types of forecasting models are econometric, time series, and artificial intelligence (AI) models. Econometric and time series models that were already popular in 2005 maintained their popularity, and were increasingly used as benchmark models for forecasting performance assessment and comparison with new models. In the last decade, AI models have advanced at an incredible rate, with hybrid AI models emerging as a new trend. In addition, some new developments in the three categories of models, such as mixed frequency, spatial regression, and combination and hybrid models have been introduced. The main conclusions drawn from historical comparisons forecasting methods are that forecasting models have become more diverse, that these models have been merged, and that forecasting accuracy has improved. Given the complexities of predicting tourism demand, there is no single approach that works well in all circumstances, and forecasting techniques are still evolving.

Tourism Demand Forecasting Based on a Neuro-Fuzzy Model

2014 ◽  
Vol 1 (1) ◽  
pp. 60-69 ◽  
Author(s):  
George Atsalakis ◽  
Eleni Chnarogiannaki ◽  
Consantinos Zopounidis
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Time Series Models ◽  
Tourism Demand ◽  
Anfis Model ◽  
Inference System ◽  
Forecasting Models ◽  
Neuro Fuzzy

Tourism in Greece plays a major role in the country's economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in making the forecasts. The data used as input for the forecasting models relates to monthly time-series tourist arrivals by air, train, sea and road into Greece from January 1996 until September 2011. 80% of the data has been used to train the forecasting models and the rest to evaluate the models. The performance of the model is achieved by the calculation of some well known statistical errors. The accuracy of the ANFIS model is further compared with two conventional forecasting models: the autoregressive (AR) and autoregressive moving average (ARMA) time-series models. The results were satisfactory even if the collected data were not pleasing enough. The ANFIS performed further compared to the other time-series models. In conclusion, the accuracy of the ANFIS model forecast proved its great importance in tourism demand forecasting.

Tourism Demand Forecasting Based on a Neuro-Fuzzy Model

2014 ◽  
pp. 106-115
Author(s):  
George Atsalakis ◽  
Eleni Chnarogiannaki ◽  
Consantinos Zopounidis
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Time Series Models ◽  
Tourism Demand ◽  
Anfis Model ◽  
Forecasting Models ◽  
Neuro Fuzzy

Tourism in Greece plays a major role in the country's economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in making the forecasts. The data used as input for the forecasting models relates to monthly time-series tourist arrivals by air, train, sea and road into Greece from January 1996 until September 2011. 80% of the data has been used to train the forecasting models and the rest to evaluate the models. The performance of the model is achieved by the calculation of some well known statistical errors. The accuracy of the ANFIS model is further compared with two conventional forecasting models: the autoregressive (AR) and autoregressive moving average (ARMA) time-series models. The results were satisfactory even if the collected data were not pleasing enough. The ANFIS performed further compared to the other time-series models. In conclusion, the accuracy of the ANFIS model forecast proved its great importance in tourism demand forecasting.

Data source combination for tourism demand forecasting

2019 ◽  
Vol 26 (7) ◽  
pp. 1248-1265 ◽  
Author(s):  
Mingming Hu ◽  
Haiyan Song
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Search Engine ◽  
Demand Forecasting ◽  
Data Models ◽  
Time Series Models ◽  
Tourism Demand ◽  
Forecasting Performance ◽  
Data Source ◽  
Causal Variables

Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models.

Analysis of Time Series Models for Brazilian Electricity Demand Forecasting

2021 ◽  
Author(s):  
Carlos Eduardo Velasquez Cabrera ◽  
Matheus Zocatelli ◽  
Fidellis B.G.L. e Estanislau ◽  
Victor Faria
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Time Series Models ◽  
Electricity Demand Forecasting ◽  
Analysis Of Time Series

Pooling in Tourism Demand Forecasting

2018 ◽  
Vol 58 (7) ◽  
pp. 1161-1174 ◽  
Author(s):  
Wen Long ◽  
Chang Liu ◽  
Haiyan Song
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Demand Forecasting ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Spatial Effects ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Demand Models ◽  
Forecasting Performance

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

scholarly journals A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION

2012 ◽  
Vol 13 (2) ◽  
pp. 275-293 ◽  
Author(s):  
A. Nazif Çatık ◽  
Mehmet Karaçuka
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Unobserved Components ◽  
Inflation Forecasting ◽  
Long Run ◽  
Univariate Time Series ◽  
Forecasting Performance ◽  
Unobserved Components Model ◽  
One Step ◽  
Conventional Models

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run.

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

scholarly journals AUTOREGRESSIVE PROCESSES AND GRAIN YIELD FORECASTING IN THE NORTHERN KAZAKHSTAN

2018 ◽  
pp. 98-105
Author(s):  
T. A. Kussaiynov ◽  
A. A. Bulasheva ◽  
Zh. O. Zhakupova
Keyword(s):  
Time Series ◽  
Grain Yield ◽  
Crop Yields ◽  
Linear Trend ◽  
Statistical Properties ◽  
Time Series Models ◽  
Autoregressive Processes ◽  
Agricultural Economy ◽  
The Past ◽  
The North

Time series models are one of the most commonly used forecasting tools in the agricultural economy. In this case, the future values of the variable are function of the past values of the same variable. In other words, there are autoregressive processes. The dynamic of grain yields in the North-Kazakhstan and Kostanay regions of Kazakhstan demonstrate very similar statistical properties. In both cases, there is a positive linear trend, the cyclical development of the process is clearly discernible. Serious attention should also be given to the existence of a cycle in the dynamics of the dispersion level of crop yields. These stochastic features of the indicator should be taken into account in agricultural forecasting.

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