Impact of decomposition on time series bagging forecasting performance

Liu, Allen, Liu, Anyu, Chen, Jason Li and Li, Gang (2023) Impact of decomposition on time series bagging forecasting performance. Tourism Management, 97. ISSN 0261-5177

Full text not available from this repository. (Request a copy)

Abstract

Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts.

Item Type:	Article
Uncontrolled Keywords:	autocorrelation,bagging,decomposition,time series forecasting,tourism demand,development,transportation,tourism, leisure and hospitality management,strategy and management ,/dk/atira/pure/subjectarea/asjc/3300/3303
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	28 May 2026 16:08
Last Modified:	01 Jun 2026 08:21
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103199
DOI:	10.1016/j.tourman.2023.104725

Actions (login required)

View Item