About: Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach
Creator	Bennbaia, Shada Mudassir, Mohammed Devrim Unal, • Hammoudeh, •
Source	Medline; PMC
abstract	Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh–ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.
has issue date	2020-07-04(xsd:dateTime)
bibo:doi	10.1007/s00521-020-05129-6
bibo:pmid	32836901
has license	no-cc
sha1sum (hex)	bb8dd6d45462671d99b345ba5744c6f1859f01b1
schema:url	https://doi.org/10.1007/s00521-020-05129-6
resource representing a document's title	Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach
has PubMed Central identifier	PMC7334635
has PubMed identifier	32836901
schema:publication	Neural Comput Appl
resource representing a document's body	covid:bb8dd6d45462671d99b345ba5744c6f1859f01b1#body_text
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