Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks

Bhaskar Tripathi () and Rakesh Kumar Sharma ()
Additional contact information
Bhaskar Tripathi: Thapar Institute of Engineering and Technology
Rakesh Kumar Sharma: Thapar Institute of Engineering and Technology

Computational Economics, 2023, vol. 62, issue 4, No 20, 1919-1945

Abstract: Abstract Bitcoin is a volatile financial asset that runs on a decentralized peer-to-peer Blockchain network. Investors need accurate price forecasts to minimize losses and maximize profits. Extreme volatility, speculative nature, and dependence on intrinsic and external factors make Bitcoin price forecast challenging. This research proposes a reliable forecasting framework by reducing the inherent noise in Bitcoin time series and by examining the predictive power of three distinct types of predictors, namely fundamental indicators, technical indicators, and univariate lagged prices. We begin with a three-step hybrid feature selection procedure to identify the variables with the highest predictive ability, then use Hampel and Savitzky–Golay filters to impute outliers and remove signal noise from the Bitcoin time series. Next, we use several deep neural networks tuned by Bayesian Optimization to forecast short-term prices for the next day, three days, five days, and seven days ahead intervals. We found that the Deep Artificial Neural Network model created using technical indicators as input data outperformed other benchmark models like Long Short Term Memory, Bi-directional LSTM (BiLSTM), and Convolutional Neural Network (CNN)-BiLSTM. The presented results record a high accuracy and outperform all existing models available in the past literature with an absolute percentage error as low as 0.28% for the next day forecast and 2.25% for the seventh day for the latest out of sample period ranging from Jan 1, 2021, to Nov 1, 2021. With contributions in feature selection, data-preprocessing, and hybridizing deep learning models, this work contributes to researchers and traders in fundamental and technical domains.

Keywords: Time series forecasting; Deep learning; Bayesian optimization; Savitzky–Golay Filter; Outlier detection (search for similar items in EconPapers)
JEL-codes: C32 C45 C61 E27 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-022-10325-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:62:y:2023:i:4:d:10.1007_s10614-022-10325-8

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-022-10325-8

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().