A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction-Reference-Cited by-同舟云学术

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A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction

Published:2022-11 Issue:11 Volume:8 Page:e11862
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Oyewola David Opeoluwa^ORCID,Dada Emmanuel Gbenga,Ndunagu Juliana Ngozi

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference42 articles.

1. Time series analysis of cryptocurrencies using deep learning & fbprophet;Indulkar,2021

2. Modelling crypto-currencies financial time-series;Catania,2017

3. Predicting the volatility of cryptocurrency time-series;Catania,2018

4. Portfolio optimization in the era of digital financialization using cryptocurrencies;Ma;Technol. Forecast. Soc. Change,2020

5. Bitcoin: a peer-to-peer electronic cash system;Nakamoto;Decentraliz. Bus. Rev.,2008

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1. Bitcoin Price Prediction Using Deep Bayesian LSTM With Uncertainty Quantification: A Monte Carlo Dropout–Based Approach;Stat;2024-09

2. Hybrid Deep Learning Model Integrating Attention Mechanism for the Accurate Prediction and Forecasting of the Cryptocurrency Market;Operations Research Forum;2024-03-01

3. The Improved Automotive Network Optimization Scheme Through Time Series Analysis Using AI/ML;2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC);2024-01-29

4. A novel granular decomposition based predictive modeling framework for cryptocurrencies' prices forecasting;China Finance Review International;2024-01-08

5. Using Big Data Analytics and Heatmap Matrix Visualization to Enhance Cryptocurrency Trading Decisions;Applied Sciences;2023-12-23

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