<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our work fits into the very important topic of predicting the value of the cryptocurrency exchange rate, and makes use of recent data which, as a result of the high Bitcoin exchange rate dynamics of the last year, differs significantly from those of previous years. We propose and test a number of neural network-based architectures and conduct a discussion of the results. Unlike previous state-of-the-art works, we conducted a comprehensive comparison of three different neural network-based models: MLP (multilayer perceptron), LSTM (long short-term memory) and CNN (convolutional neural network). We tested them for a wide range of parameters. The results we present are, to the best of our knowledge, the most up to date when it comes to the application of artificial intelligence methods for the prediction of cryptocurrency exchange rates. The best-performing architectures were used for a website that gives real-time predictions of the Bitcoin exchange rate. The website is available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://stpbtc-ii.up.krakow.pl/">http://stpbtc-ii.up.krakow.pl/</ext-link>. Source codes of our research are available to download in order to make our experiment reproducible.</p>
</abstract>

The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our work fits into the very important topic of predicting the value of the cryptocurrency exchange rate, and makes use of recent data which, as a result of the high Bitcoin exchange rate dynamics of the last year, differs significantly from those of previous years. We propose and test a number of neural network-based architectures and conduct a discussion of the results. Unlike previous state-of-the-art works, we conducted a comprehensive comparison of three different neural network-based models: MLP (multilayer perceptron), LSTM (long short-term memory) and CNN (convolutional neural network). We tested them for a wide range of parameters. The results we present are, to the best of our knowledge, the most up to date when it comes to the application of artificial intelligence methods for the prediction of cryptocurrency exchange rates. The best-performing architectures were used for a website that gives real-time predictions of the Bitcoin exchange rate. The website is available at http://stpbtc-ii.up.krakow.pl/. Source codes of our research are available to download in order to make our experiment reproducible.

How many neurons are needed to make a short-term prediction of the Bitcoin exchange rate?

Technical Transactions

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our...

How many neurons are needed to make a short-term prediction of the Bitcoin exchange rate?

Data publikacji: 23 sie 2022

Zakres stron: -

Otrzymano: 07 mar 2022

Przyjęty: 09 sie 2022

DOI: https://doi.org/10.37705/TechTrans/e2022011

Słowa kluczowe
neural network, multiline perceptron, convolutional neural network, long short-term memory, Bitcoin

© 2022 Michał Frontczak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

How many neurons are needed to make a short-term prediction of the Bitcoin exchange rate?

Data publikacji: 23 sie 2022

Zakres stron: -

Otrzymano: 07 mar 2022

Przyjęty: 09 sie 2022

DOI: https://doi.org/10.37705/TechTrans/e2022011

Słowa kluczoweneural network, multiline perceptron, convolutional neural network, long short-term memory, Bitcoin

© 2022 Michał Frontczak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
neural network, multiline perceptron, convolutional neural network, long short-term memory, Bitcoin