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The Influence of Bitcoin on Ethereum Price Predictions
Mälardalen University, School of Innovation, Design and Engineering.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Cryptocurrencies are a cryptography based technology, that has increased massively in popularity in recent years. These currencies are traded on markets that specialize in cryptocurrency trade. There, you can trade one cryptocurrency for another, or buy one with real world money. These markets are quite volatile, meaning that the price of most cryptocurrencies swing up and down a lot. The largest cryptocurrency is Bitcoin, but there is also more than 1500 smaller ones, that goes by the name alternative coins, or altcoins. This thesis will try to find out if it is possible to make accurate predictions about the future price of the altcoin Ethereum, and also see if Bitcoin may have some influence over the price of the selected altcoin. The predictions were made with the use of an artificial neural network, an LSTM network, that was trained on labeled data from 2017. The predictions were then made in intervals of one hour ahead, six hours ahead, and one day ahead through early 2018. The predictions showed that it is possible to make somewhat accurate predictions about the future. The predictions that were made one hour ahead were more accurate than both the six hours ahead predictions and the full day ahead predictions. By comparing the loss rates of the neural networks that were only trained on Ethereum, with the loss rates of the networks that trained on both Bitcoin and Ethereum, is was made clear that training on both cryptocurrencies did not improve the prediction accuracies.

Place, publisher, year, edition, pages
2018.
Keywords [en]
bitcoin, ethereum, crypto, cryptocurrency, predictions, price predictions, bitcoin predictions, thereum predictions, machine learning, neural networks, rnn, lstm
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-40065OAI: oai:DiVA.org:mdh-40065DiVA, id: diva2:1225270
Subject / course
Computer Science
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Available from: 2018-07-01 Created: 2018-06-26 Last updated: 2018-07-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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More languages
Output format
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