An application of hybrid forecasting singular spectrum analysis – extreme learning machine method in foreign tourists forecasting

Muhammad Fajar

MPRA Paper from University Library of Munich, Germany

Abstract: International tourism is one indicator of measuring tourism development. Tourism development is important for the national economy since tourism could boost foreign exchange, create business opportunities, and provide employment opportunities. The prediction of foreign tourist numbers in the future obtained from forecasting is used as an input parameter for strategy and tourism programs planning. In this paper, the Hybrid Singular Spectrum Analysis – Extreme Learning Machine (SSA-ELM) is used to forecast the number of foreign tourists. Data used is the number of foreign tourists January 1980 - December 2017 taken from Badan Pusat Statistik (Statistics Indonesia). The result of this research concludes that Hybrid SSA-ELM performance is very good at forecasting the number of foreign tourists. It is shown by the MAPE value of 4.91 percent with eight observations out a sample.

Keywords: foreign tourist; singular spectrum analysis; extreme learning machine (search for similar items in EconPapers)
JEL-codes: C22 C45 C51 E17 (search for similar items in EconPapers)
Date: 2019-10-31, Revised 2019-10-31
New Economics Papers: this item is included in nep-big, nep-for, nep-sea and nep-tur
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Published in Jurnal Matematika MANTIK 2.5(2019): pp. 60-68

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:105044

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