Abstract
Bitcoin is a worldwide virtual currency. It is globally used as a financial asset and as a currency factor for buying and selling products and services in exchange of fractions or numbers of Bitcoins. Bitcoin is not owned by a lone authority or organization but rather is decentralized. Bitcoins can be sold, bought, or traded on platforms called “Bitcoin exchanges.” Exchanges permit individuals to buy/trade/sell Bitcoins using an array of currencies using a P2P (i.e., peer-to-peer) network. This paper presents regression models for determining close price of Bitcoin in Bitstamp exchange based on characteristics of the cryptocurrency and blockchain information. Two live datasets have been acquired that provide real-time data consisting of various attributes of Bitcoin price and blockchain information and the two datasets have been combined to prepare final dataset for the investigation. Times-series model of the live data has been developed. The motive of this research is to provide an insight on prediction of Bitcoin close price. The underlying catalyst for implementing machine learning techniques is to meticulously forecast time-series data. Machine learning techniques have better demonstrated to outplay nonlinear techniques including neural network-based algorithms. The ultimate goal is to contribute an observation into the applications of various predictive models that can be used to predict Bitcoin prices.
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Seraphim, B.I., Dash, S., Ambastha, K., Sowmiya, B. (2021). Determining the Close Price of Bitcoin Using Regression Based on Blockchain Information. In: Dash, S.S., Panigrahi, B.K., Das, S. (eds) Sixth International Conference on Intelligent Computing and Applications . Advances in Intelligent Systems and Computing, vol 1369. Springer, Singapore. https://doi.org/10.1007/978-981-16-1335-7_33
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DOI: https://doi.org/10.1007/978-981-16-1335-7_33
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