Bitcoin Forecasting Using ARIMA and PROPHET

dc.contributor.authorYenidoğan, Işıl
dc.contributor.authorÇayır, Aykut
dc.contributor.authorKozan, Ozan
dc.contributor.authorDağ, Tugce
dc.contributor.authorArslan, Çiğdem
dc.date.accessioned2019-06-27T08:01:06Z
dc.date.available2019-06-27T08:01:06Z
dc.date.issued2018
dc.departmentFakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.description.abstractThis paper presents all studies methodology and results about Bitcoin forecasting with PROPHET and ARIMA methods using R analytics platform. To find the most accurate forecast model the performance metrics of PROPHET and AMNIA methods are compared on the same dataset. The dataset selected 16r this study starts from May 2016 and ends in March 2018 which is the interval that Bitcoin values changing significantly against the other currencies. Data is prepared for time series analysis by performing data preprocessing steps such as time stamp conversion and feature selection. Although the time series analysis has a univariate characteristics it is aimed to include some additional variables to each model to improve the forecasting accuracy. Those additional variables are selected based on different correlation studies between cryptocurrencies and real currencies. The model selection for both ARIMA and PROPHET is done by using threefold splitting technique considering the time series characteristics of the dataset. The threefold splitting technique gave the optimum ratios for training validation and test sets. Filially two different models are created and compared in terms of performance metrics. Based on the extensive testing we see that PROPHET outperforms ARIMA by 0.94 to 0.68 in R-2 values.en_US]
dc.identifier.citation37
dc.identifier.doi10.1109/UBMK.2018.8566476en_US
dc.identifier.endpage624
dc.identifier.isbn978-1-5386-7893-0
dc.identifier.scopus2-s2.0-85060636848en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage621en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/255
dc.identifier.urihttps://doi.org/10.1109/UBMK.2018.8566476
dc.identifier.wosWOS:000459847400119en_US
dc.identifier.wosqualityN/A
dc.institutionauthorYenidoğan, Işılen_US
dc.institutionauthorÇayır, Aykuten_US
dc.institutionauthorKozan, Ozanen_US
dc.institutionauthorDağ, Tuğçeen_US
dc.institutionauthorArslan, Çiğdemen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.journal3rd International Conference on Computer Science and Engineering (UBMK)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBitcoinen_US
dc.subjectFarcastingen_US
dc.titleBitcoin Forecasting Using ARIMA and PROPHETen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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