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dc.contributor.authorYenidogan, Isil
dc.contributor.authorÇayir, Aykut
dc.contributor.authorKozan, Ozan
dc.contributor.authorDag, Tugce
dc.contributor.authorArslan, Cigdem
dc.date.accessioned2019-06-27T08:01:06Z
dc.date.available2019-06-27T08:01:06Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-7893-0
dc.identifier.urihttps://hdl.handle.net/20.500.12469/255
dc.identifier.urihttps://doi.org/10.1109/UBMK.2018.8566476
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.
dc.language.isoEnglish
dc.publisherIEEE
dc.subjectBitcoin
dc.subjectFarcasting
dc.titleBitcoin Forecasting Using ARIMA and PROPHET
dc.typeProceedings Paper
dc.identifier.startpage621
dc.identifier.endpage624
dc.relation.journal3rd International Conference on Computer Science and Engineering (UBMK)
dc.identifier.wosWOS:000459847400119
dc.identifier.doi10.1109/UBMK.2018.8566476
dc.contributor.khasauthorYenidogan, Isil
dc.contributor.khasauthorÇayir, Aykut
dc.contributor.khasauthorKozan, Ozan
dc.contributor.khasauthorDag, Tugce
dc.contributor.khasauthorArslan, Cigdem


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