Assignment Complete.Running head: ARTICLE REVIEW 11Article Review 1NameInstitutional AffiliationARTICLE REVIEW 12Article Review 1Tourism Demand Modelling and Forecasting (2008)The article Tourism Demand Modelling and Forecasting: A Review of Recent Researchby Song & Li (2008) presents a review of published studies on demand forecasting in tourismfrom 2000. It presents empirical findings of the research, which is based on methodologicaldevelopments, competition, combination, and integration of forecasts, and some generalobservations. In this review, the writer presents a summary of the article, the association betweenthe lessons of the article with the lessons in class. The writer concludes by presenting hisperspectives of the article.Article SummaryThe article presents a review of published studies that majored in tourism demand andforecasting from 2000. The increasing demand for tourism worldwide over the previous twentyyears has compelled scholars to research demand and forecasting in tourism. The findings drawnfrom these studies suggest that the methods for analyzing as well as forecasting demand are morediverse as compared to other review articles. In addition to econometric models, various newmethods are presented in the literature. Nevertheless, when it comes to forecasting accuracy, thestudy makes it clear that no single model performs better than the other in various situations.Moreover, this research identifies various newfangled research directions, which encompassoptimizing the forecasting precision through prediction amalgamation, combining bothquantitative and qualitative forecasting approaches, seasonality analysis, tourism cycles, impactassessment events, as well as risk forecasting. All the same, seasonality analysis is usuallyacknowledged when performing an analysis of tourism demand. The reason behind this is thatthe market in this sector is marked with seasonal demand. Seasonal demand is a particular timeARTICLE REVIEW 13series with predictable or repetitive demand patterns because of the reoccurrence of the seasonalevents. These patterns can reoccur for some days, months, or even quarters, and this may hencemake it difficult for businesses to project imminent demand trends. The researchers gaveexamples of some seasonal events in the United Kingdom (UK) that occur yearly. Someexamples of these events are Ramadan, Christmas, yearly events like Bonfire Night andValentines’ Day, and seasonal weather patterns like the hot climate in summer and snow amidwinter.Song & Li (2008) make it clear that forecasters normally prefer to forecast demand byconsidering the seasonal analysis model, which enables when applied in businesses, it promotestheir competitiveness in the market. Various reasons suggest why forecasters prefer seasonalanalysis when performing demand forecasting. Employing the model enables them to drawmeaningful outcomes which first, allows investors to capitalize on the peaks in demand. Thereason behind this is that forecasting for seasonal discrepancies ensures that investors haveadequate stock levels available to consequently take advantage of upsurges in the demand of theproduct during peak. Subsequently, this may enable investors to enjoy substantial profits.Secondly, it restricts investors from acquiring excessive stock and issue that may contribute tocash flow problems as well as the morbid balance sheet. However, while seasonality analysisseems to be highly adopted when it comes to forecasting tourism demand, the ways in which theanalysis is handled are still obscure. Seasonal fractional integration, which was introducedrecently, is an optional approach to model seasonality. Despite this fact, scholars are should becommitted to engaging in more methods to improve forecast accuracy. While a lot of attentionhas been directed to forecasting the intensity of tourism demand, there is a need to conductextensive research on improving the accuracy of forecasting.ARTICLE REVIEW 14Song & Li (2008) present thoughtful information that is not only useful to forecasters butalso forthcoming researchers because it presents new research guide such as combining bothquantitative and qualitative forecasting methodologies, inform the readers the necessity ofintegrating models when forecasting as it helps to advance accuracy in forecasting.Connecting Lessons from the Article with Lessons in the ClassThe article presents thoughtful information regarding forecasting demand. After readingthrough the article, it became clear to me that organizations along with enterprises acknowledgedemand forecasting because it enables them to determine and project the number of services thecustomers will be willing to acquire in the foreseeable future. The article also presents variousmodels that researchers can engage to improve efficiency and accuracy when forecasting. Theresearchers of this article have made me realize that there is no ideal way in which a singlemodel can promote accuracy in forecasting when used alone. The researchers also confirmed thatseasonal fraction is the ultimate model when used alone in conducting tourism demandforecasting.While the seasonality model in determining tourism demand is often used following itseffectiveness, the researchers encourage those working in the tourism sector to considerintegrating this model with other models to improve the accuracy of demand forecasting. This isbecause by integrating the model with other, errors are minimized, and this subsequently, willenable them to identify the covariant variables which augment the degree of accuracy inforecasting.There is a connection between what I learned from the article with what in learned inclass. The article informs the reader on how to improve accuracy in demand forecasting by usingmultiple models when working towards improving the efficiency and accuracy of demandARTICLE REVIEW 15forecasting. By applying multiple models, errors will be minimized, and it would be necessary toconfirm this by engaging forecasting measurement criteria that institutes mean absolute deviation(MAD) and mean error (BIAS) where low percentages in MAD and BIAS suggest minimizederrors hence higher accuracy and efficiency in demand forecasting.My Perspectives on the ArticleMy perspectives of the article are based on the idea of how to perform demandforecasting while getting rid of the errors that may arise. However, engaging multiple models aresignificant in improving the process of demand forecasting. However, the researchers primarilymajored in forecasting the degree of tourism demand while researching a little on forecasting theturning point forecast accuracy. This points out the need to encourage future researchers to focuson this area.My New Learning from the ArticleI learned that it would be possible to promote the accuracy in demand forecasting byengaging multiple models because there is no single model that proves to be more accurate thanthe other.The Interesting…


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