Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains

Dragan Dejan (), Keshavarzsaleh Abolfazl (), Kramberger Tomaž (), Jereb Borut () and Rosi Maja ()
Additional contact information
Dragan Dejan: University of Maribor/Faculty of Logistics, Celje, Slovenia
Keshavarzsaleh Abolfazl: University of Malaya (UM) and International University of Malaya-Wales (IUMW), Kuala Lumpur, Malaysia
Kramberger Tomaž: University of Maribor/Faculty of Logistics, Celje, Slovenia
Jereb Borut: University of Maribor/Faculty of Logistics, Celje, Slovenia
Rosi Maja: University of Maribor/Faculty of Logistics, Celje, Slovenia

Logistics, Supply Chain, Sustainability and Global Challenges, 2019, vol. 10, issue 1, 11-30

Abstract: Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists’ inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists’ inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.

Keywords: Tourism industry logistics and supply chains; Tourism forecasting; US tourists and Slovenian Tourism; Damped Holt-Winters additive model; Time series analysis and prediction (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jlst-2019-0002 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:losutr:v:10:y:2019:i:1:p:11-30:n:2

DOI: 10.2478/jlst-2019-0002

Access Statistics for this article

Logistics, Supply Chain, Sustainability and Global Challenges is currently edited by Maja Fošner

More articles in Logistics, Supply Chain, Sustainability and Global Challenges from Sciendo
Bibliographic data for series maintained by Peter Golla ().