Centres Of Excellence

To focus on new and emerging areas of research and education, Centres of Excellence have been established within the Institute. These ‘virtual' centres draw on resources from its stakeholders, and interact with them to enhance core competencies

Read More >>

Faculty

Faculty members at IIMB generate knowledge through cutting-edge research in all functional areas of management that would benefit public and private sector companies, and government and society in general.

Read More >>

IIMB Management Review

Journal of Indian Institute of Management Bangalore

IIM Bangalore offers Degree-Granting Programmes, a Diploma Programme, Certificate Programmes and Executive Education Programmes and specialised courses in areas such as entrepreneurship and public policy.

Read More >>

About IIMB

The Indian Institute of Management Bangalore (IIMB) believes in building leaders through holistic, transformative and innovative education

Read More >>

A differential evolution-based regression framework for forecasting Bitcoin price

Debojyoti Das, R.K. Jana and Indranil Ghosh
Journal Name
Annals of Operations Research
Journal Publication
others
Publication Year
2021
Journal Publications Functional Area
Finance & Accounting
Publication Date
Vol. 306, 2021, Pg. 295-320
Abstract

This research proposes a differential evolution-based regression framework for forecasting one day ahead price of Bitcoin. The maximal overlap discrete wavelet transformation first decomposes the original series into granular linear and nonlinear components. We then fit polynomial regression with interaction (PRI) and support vector regression (SVR) on linear and nonlinear components and obtain component-wise projections. The sum of these projections constitutes the final forecast. For accurate predictions, the PRI coefficients and tuning of the hyperparameters of SVR must be precisely estimated. Differential evolution, a metaheuristic optimization technique, helps to achieve these goals. We compare the forecast accuracy of the proposed regression framework with six advanced predictive modeling algorithms- multilayer perceptron neural network, random forest, adaptive neural fuzzy inference system, standalone SVR, multiple adaptive regression spline, and least absolute shrinkage and selection operator. Finally, we perform the numerical experimentation based on—(1) the daily closing prices of Bitcoin for January 10, 2013, to February 23, 2019, and (2) randomly generated surrogate time series through Monte Carlo analysis. The forecast accuracy of the proposed framework is higher than the other predictive modeling algorithms.

Author(s) Name: Debojyoti Das, R.K. Jana and Indranil Ghosh
Journal Name : Annals of Operations Research
Volume : Vol. 306, 2021, Pg. 295-320
Year of Publication : 2021
Abstract :

This research proposes a differential evolution-based regression framework for forecasting one day ahead price of Bitcoin. The maximal overlap discrete wavelet transformation first decomposes the original series into granular linear and nonlinear components. We then fit polynomial regression with interaction (PRI) and support vector regression (SVR) on linear and nonlinear components and obtain component-wise projections. The sum of these projections constitutes the final forecast. For accurate predictions, the PRI coefficients and tuning of the hyperparameters of SVR must be precisely estimated. Differential evolution, a metaheuristic optimization technique, helps to achieve these goals. We compare the forecast accuracy of the proposed regression framework with six advanced predictive modeling algorithms- multilayer perceptron neural network, random forest, adaptive neural fuzzy inference system, standalone SVR, multiple adaptive regression spline, and least absolute shrinkage and selection operator. Finally, we perform the numerical experimentation based on—(1) the daily closing prices of Bitcoin for January 10, 2013, to February 23, 2019, and (2) randomly generated surrogate time series through Monte Carlo analysis. The forecast accuracy of the proposed framework is higher than the other predictive modeling algorithms.