Cryptocurrency price forecasting: a comparative analysis of autoregressive and recurrent neural network models

Joana Katina (), Joana Katina (), Igor Katin (), Igor Katin () and Vera Komarova ()
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Joana Katina: Vilnius University, Lithuania
Joana Katina: Vilniaus kolegija / Higher Education Institution, Lithuania
Igor Katin: Vilnius University, Lithuania
Igor Katin: Vilniaus kolegija / Higher Education Institution, Lithuania
Vera Komarova: Daugavpils University, Latvia

Entrepreneurship and Sustainability Issues, 2024, vol. 11, issue 4, 425-436

Abstract: This article presents a novel approach to cryptocurrency price forecasting, leveraging advanced machine-learning techniques. By comparing traditional autoregressive models with recurrent neural network approaches, the study aims to evaluate the forecasting accuracy of Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models across various cryptocurrencies, including Bitcoin, Ethereum, Dogecoin, Polygon, and Toncoin. The data for this empirical study was sourced from historical prices of these specific cryptocurrencies, as recorded on the CoinMarketCap platform, covering January 2022 to April 2024. The methodology employed involves rigorous statistical and neural network modelling where each model's parameters were meticulously optimized for the specific characteristics of each cryptocurrency's price data. Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used to assess the precision of each model. The main results indicate that LSTM and GRU models, leveraging deep learning techniques, generally outperformed the traditional ARIMA and SARIMA models regarding error metrics. This demonstrates a higher efficacy of neural networks in handling the non-linear complexities and volatile nature of cryptocurrency price movements. This study contributes to the ongoing discourse in financial technology by elucidating the practical implications of using advanced machine-learning techniques for economic forecasting. Importantly, it provides valuable insights that can directly inform and enhance the decision-making processes of investors and traders in digital assets.

Keywords: forecasting; prediction; cryptocurrencies; time series; ARIMA; SARIMA; RNN; LSTM; GRU (search for similar items in EconPapers)
JEL-codes: C22 C32 C45 C53 G17 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:11:y:2024:i:4:p:425-436

DOI: 10.9770/jesi.2024.11.4(26)

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