Forecasting Bitcoin closing price series using linear regression and neural networks models

Abstract

This paper studies how to forecast daily closing price series of Bitcoin, using data on prices and volumes of prior days. Bitcoin price behaviour is still largely unexplored, presenting new opportunities. We compared our results with two modern works on Bitcoin prices forecasting and with a wellknown recent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms. The SLR model for univariate series forecast uses only closing prices, whereas the MLR model for multivariate series uses both price and volume data. We applied the ADF -Test to these series, which resulted to be indistinguishable from a random walk. We also used two artificial neural networks: MLP and LSTM. We then partitioned the dataset into shorter sequences, representing different price "regimes", obtaining best result using more than one previous price, thus confirming our regime hypothesis. All the models were evaluated in terms of MAPE and relativeRMSE. They performed well, and were overall better than those obtained in the benchmarks. Based on the results, it was possible to demonstrate the efficacy of the proposed methodology and its contribution to the state-of-the-art.

Proceedings of the Selected and Revised Papers of 9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2021). Odessa, Ukraine, May 26-28, 2021

“…Based on the obtained results, recommendations of the management of the researched enterprises on increase of management efficiency are given. This article highlights further research by the authors, begun in [50,51,52,53,54,55].…”

Econophysics of cryptocurrency crashes: a systematic review

“…Equally interesting is the development of the theory of quantum computation and quantum information [376,377,378,379] where it would be interesting to see their influence on blockchain and cryptocurrencies. Moreover, the research in the field of artificial intelligence, machine, and deep learning does not remain without attention [380,381,52,382,383,384,385,386,387,388,389,390,391].…”

“…Hence, the cryptocurrency prices exhibit such complex volatility characteristics as nonlinearity and uncertainty, which are difficult to forecast, and any obtained results are uncertain. Therefore, cryptocurrency price prediction remains a huge challenge [45,46,47,48,49,50,51,52,53,54,55,56,57,58,59].…”

MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price

“…LSTM neural networks [14] have been shown to be significantly effective in forecasting bitcoin prices due to their ability to identify long-term dependencies and store both long-term and short-term temporal information. Researchers have obtained better results with the LSTM network, in their works to predict bitcoin prices and compare them with the performance of different models [15], [16].…”