Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features

Gyana Ranjan Patra () and Mihir Narayan Mohanty ()
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Gyana Ranjan Patra: Siksha ‘O’ Anusandhan (Deemed to Be University)
Mihir Narayan Mohanty: Siksha ‘O’ Anusandhan (Deemed to Be University)

Computational Economics, 2023, vol. 62, issue 4, No 7, 1525-1544

Abstract: Abstract In today’s world the cryptocurrencies have taken a special place in the financial market with the aid of time series forecasting and with a market value of almost USD 1.5 Trillion as of August 2021. These financial commodities show more volatility and effect of different internal and external forces. The prediction of such cryptocurrencies is a time consuming and difficult affair. In this paper, an attempt is made with a multilayer Gated Recurrent Unit based model for price prediction of cryptocurrencies. The currencies considered in this work are Bitcoin, Ethereum and Dogecoin and are pre-processed to deal with the NaN values. The LSTM model, one variant of recurrent neural network is used initially for predication. Further, the Gated Recurrent Unit is used with single feature. However, it is observed that for multiple features with three layers of Gated Recurrent Units based model is working well with error minimization. The performance of the proposed model is compared with other two models over a 21-day forecasting window. The proposed model is found to provide better performance in terms of different parameters like mean square error, root mean square error, mean absolute error, mean absolute percentage error, p-value, and precision values than the other two models.

Keywords: Crypto-currency; Prediction; Gated recurrent unit; Long short-term memory; Mean absolute percentage error (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10310-1

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