A novel hybrid walk-forward ensemble optimization for time series cryptocurrency prediction
David Opeoluwa Oyewola1, Emmanuel Gbenga Dada2, Juliana Ngozi Ndunagu3
1Department of Mathematics and Statistics, Federal Univerisity Kashere, Gombe, Nigeria.
Heliyon
|December 2, 2022
Summary
Predicting cryptocurrency prices is challenging. A new hybrid ensemble optimization technique shows improved accuracy for daily price predictions of major cryptocurrencies like Bitcoin and Ethereum.
Area of Science:
- * Digital Finance and Computational Economics
- * Time Series Analysis and Predictive Modeling
Background:
- * Cryptocurrencies are advanced digital currencies secured by encryption, often built on decentralized blockchain networks.
- * Accurate cryptocurrency price prediction is difficult due to the lack of a solid analytical basis and influence from numerous variables.
- * Factors influencing cryptocurrency prices include technical advancements, market competition, economic conditions, security, and political considerations.
Purpose of the Study:
- * To propose and evaluate a novel hybrid walk-forward ensemble optimization technique for cryptocurrency price prediction.
- * To assess the predictive performance of the proposed model against traditional and advanced algorithms across various cryptocurrencies.
- * To compare the accuracy of predicting daily prices for fifteen selected cryptocurrencies.
Main Methods:
- * Development and application of a hybrid walk-forward ensemble optimization technique.
- * Prediction of daily prices for fifteen cryptocurrencies: Cardano, Bitcoin, Dogecoin, Ethereum Classic, Chainlink, Litecoin, NEO, Tron, Tether, NEM, Stellar, Ripple, and Tezos.
- * Comparative analysis using classical statistical models, machine learning algorithms, and deep learning algorithms on cryptocurrency time series data.
Main Results:
- * The proposed hybrid walk-forward ensemble optimization technique demonstrated superior performance in cryptocurrency price prediction.
- * The model achieved higher accuracy compared to classical statistical models, machine learning, and deep learning algorithms.
- * Simulation results validated the effectiveness of the ensemble approach for diverse cryptocurrency time series.
Conclusions:
- * The hybrid walk-forward ensemble optimization technique offers a more accurate approach to predicting cryptocurrency prices.
- * This method provides a valuable tool for navigating the complexities of the volatile cryptocurrency market.
- * The findings suggest a promising direction for enhancing predictive accuracy in digital asset markets.
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