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A two level ensemble classification approach to forecast bitcoin prices

Harish Kundra (Department of Computer Science and Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India)
Sudhir Sharma (Department of Information Technology, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, India)
P. Nancy (Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India)
Dasari Kalyani (Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India)

Kybernetes

ISSN: 0368-492X

Article publication date: 19 July 2022

Issue publication date: 9 November 2023

149

Abstract

Purpose

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

Design/methodology/approach

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

Findings

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

Originality/value

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

Keywords

Acknowledgements

Data availability statement: Data sharing is not applicable to this article as no new data were created or analyzed in this study.

The author would like to express great appreciation to the co-authors of this manuscript for the valuable and constructive suggestions during the planning and development of this research work.

Funding: This research did not receive any specific funding.

Citation

Kundra, H., Sharma, S., Nancy, P. and Kalyani, D. (2023), "A two level ensemble classification approach to forecast bitcoin prices", Kybernetes, Vol. 52 No. 11, pp. 5041-5067. https://doi.org/10.1108/K-11-2021-1213

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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