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Bitcoin Price Prediction: An ARIMA Approach
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab). (COS)
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Bitcoin is considered as the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-cryptoeconomic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA)model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction,e.g. 1-day. On the other hand, when we try to train the Arima model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model trained over the whole dataset, as well as a limited part of the history of the bitcoin price, with length of w, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of (p; q; d), and window size w.

Keywords [en]
Bitcoin, Crypto-concurrency, Estimation, Data Science, ARIMA
Keywords [fa]
تخمین قیمت بیتکوین، مدل آریما
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-238673OAI: oai:DiVA.org:kth-238673DiVA, id: diva2:1261399
Note

QC 20181107

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-07Bibliographically approved

Open Access in DiVA

fulltext(344 kB)91 downloads
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Azari, Amin
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Radio Systems Laboratory (RS Lab)
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf