Forecasting tourism recovery amid COVID-19: a comparison between bottom-up and non-overlapping temporal aggregation

Yilu Zheng and Konstantinos Nikolopoulos

Chapter 3 in Forecasting, Planning and Strategy in a Turbulent Era, 2025, pp 96-133 from Edward Elgar Publishing

Abstract: Abstrac t This chapter explores the essential nature of tourism forecasting and addresses the following question: in NOA, how is the prediction accuracy of models affected by different levels of aggregation (daily, weekly, monthly)? It compares the performance between Bottom-Up Aggregate (BU) and Non-overlapping temporal aggregation (NOA), and compares the predictive abilities of the ETS and SARIMA models under the aggregation method.

Keywords: Tourism; NOA; SARIMA models; ETS models; COVID-19; Hawaii (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035317233
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