A practitioners guide to time-series methods for tourism demand forecasting — a case study of Durban, South Africa

2001 ◽  
Vol 22 (4) ◽  
pp. 403-409 ◽  
Author(s):  
C.J.S.C Burger ◽  
M Dohnal ◽  
M Kathrada ◽  
R Law
Keyword(s):  
South Africa ◽  
Time Series ◽  
Demand Forecasting ◽  
Tourism Demand

Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series

2013 ◽  
Vol 43 ◽  
pp. 112-122 ◽  
Author(s):  
Jamal Shahrabi ◽  
Esmaeil Hadavandi ◽  
Shahrokh Asadi
Keyword(s):  
Time Series ◽  
Tourism Demand ◽  
Intelligent Model

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.

scholarly journals Forecasting Seasonal Tourism Demand Using a Multiseries Structural Time Series Method

2017 ◽  
Vol 58 (1) ◽  
pp. 92-103 ◽  
Author(s):  
Jason Li Chen ◽  
Gang Li ◽  
Doris Chenguang Wu ◽  
Shujie Shen
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Series Method ◽  
Tourism Demand ◽  
Multivariate Method ◽  
Structural Time Series ◽  
Alternative Approach ◽  
Time Series Method ◽  
Structural Time

Multivariate forecasting methods are intuitively appealing since they are able to capture the interseries dependencies, and therefore may forecast more accurately. This study proposes a multiseries structural time series method based on a novel data restacking technique as an alternative approach to seasonal tourism demand forecasting. The proposed approach is analogous to the multivariate method but only requires one variable. In this study, a quarterly tourism demand series is split into four component series, each component representing the demand in a particular quarter of each year; the component series are then restacked to build a multiseries structural time series model. Empirical evidence from Hong Kong inbound tourism demand forecasting shows that the newly proposed approach improves the forecast accuracy, compared with traditional univariate models.

scholarly journals Modelling Prediction of Consumer Demand in the Tourism and Hospitality Based on Time Series

2019 ◽  
Vol 8 (4) ◽  
pp. 8551-8558 ◽  
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Multivariate Regression Analysis ◽  
Tourism Demand ◽  
Limited Data ◽  
Qualitative Approaches ◽  
Load Factors ◽  
Accurate Forecast ◽  
Tourist Demand ◽  
Tourism And Hospitality

Travel services, unlike other services, cannot be stored or stockpiled for the future. Unsold hotel rooms, excursions or unfilled seats on the aeroplane cannot be sold over time. When real demand provides planned load factors, the business grows. This indicates the importance of demand forecasting for all tourism enterprises.In forecasting tourism demand, quantitative and qualitative approaches are used. A quantitative approach is based on statistical information for the previous period, and a qualitative one is based on people's opinions and opinions. Multivariate regression analysis is the most popular model for forecasting tourist demand. It takes into account many factors on which the tourist flow depends. In conditions of limited data, a time series model is used, which gives a high forecast, especially in pronounced seasonality. For a more accurate forecast of tourism demand, it is necessary to combine quantitative and qualitative approaches.

scholarly journals Identifying an Appropriate Forecasting Technique for Predicting Future Demand: A Case Study on a Private University of Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 15-19
Author(s):  
Sayma Suraiya ◽  
M Babul Hasan
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Moving Average ◽  
Demand Forecasting ◽  
Private University ◽  
Planning Horizon ◽  
Time Series Forecasting ◽  
Base Level ◽  
Printing Paper

Demand forecasting and inventory control of printing paper is crucial that is frequently used every day for the different purposes in all sectors of educational area especially in Universities. A case study is conducted in a University store house to collect all historical demand data of printing papers for last 6 years (18 trimesters), from January (Spring) 2011 to December (Fall) 2016. We will use the different models of time series forecasting which always offers a steady base-level forecast and is good at handling regular demand patterns. The aim of the research paper is to find out the less and best error free forecasting techniques for the demand of printing paper for a particular time being by using the quantitative forecasting or time series forecasting models like weighted moving average, 3-point single moving average, 3-point double moving average, 5-point moving average, exponential smoothing, regression analysis/linear trend, Holt’s method and Winter’s method. According to the forecasting error measurement, we will observe in this research that the best forecasting technique is linear trend model. By using the quantities of data and drawing the conclusion with an acceptable accuracy, our analysis will help the university to decide how much inventory is absolutely needed for the planning horizon. Dhaka Univ. J. Sci. 66(1): 15-19, 2018 (January)

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.

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