ReviewNeural computing & applications2020
Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach.
Review in Neural computing & applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Electricity price forecasting with ensemble meta-models and SHAP explainers: a PCA-driven approach.Scientific reports · 2026Article
- Evaluating machine learning models for predictive accuracy in cryptocurrency price forecasting.PeerJ. Computer science · 2025Article
- A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness.Health care science · 2024Article
- A performance comparison of machine learning models for stock market prediction with novel investment strategy.PloS one · 2023Article
- A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction.Heliyon · 2022Article
- Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks.Computational economics · 2022Article
- Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines.Sensors (Basel, Switzerland) · 2022Article
- Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis.Financial innovation · 2022Article
- Predictions of bitcoin prices through machine learning based frameworks.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.