Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm

The tourism industry has been integrated into the national strategic system in China. Thus, tourism demand forecasting has become a concern for the sustainable development of the tourism industry. Unfortunately, the sample size for tourism in China is always small and cannot satisfy the hypothesis t...

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Vydáno v:	Applied soft computing Ročník 92; s. 106320
Hlavní autoři:	Jiang, Ping, Yang, Hufang, Li, Ranran, Li, Chen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.07.2020
Témata:	Atom search optimization algorithm Fuzzy time series model Inbound tourism demand Information optimization technology Small sample forecasting Information optimization technology Inbound tourism demand Atom search optimization algorithm Small sample forecasting Fuzzy time series model
ISSN:	1568-4946, 1872-9681
On-line přístup:	Získat plný text
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Popis
Shrnutí:	The tourism industry has been integrated into the national strategic system in China. Thus, tourism demand forecasting has become a concern for the sustainable development of the tourism industry. Unfortunately, the sample size for tourism in China is always small and cannot satisfy the hypothesis test of an economic model or the data volume for a traditional time series model. In this study, a novel hybrid forecasting framework combining fuzzy time series (FTS) and an atom search optimization (ASO) algorithm is proposed for inbound tourism demand forecasting; this forecasting framework is particularly suitable for small sample sizes. Specifically, information optimization technology is applied in the FTS to improve the recognition ability of the system and effectively identify small sample information. The ASO algorithm is applied to search the optimal parameters of FTS that can further improve forecasting performance. All comparison experiments and tests verify the effectiveness and superiority of our proposed model, which provides excellent forecasting results for tourism demand and a basis for policymakers and managers to plan appropriately for the tourism market. •Develop a novel hybrid forecasting framework focusing on small sample forecasting.•Improve the system’s recognition ability by information optimization technology.•Search the optimal parameters based on atom search optimization algorithm.•Identify the regional characteristics of inbound tourism demand based on FCM.•Analyze the inbound tourism demand based out-of-sample forecasting results.
ISSN:	1568-4946 1872-9681
DOI:	10.1016/j.asoc.2020.106320