Optimizing Cryptocurrency Price Prediction: A Hybrid Approach with Resilient Stochastic Clustering and Gravitational Search Algorithm
Keywords:
Oppositional Sparrow Search, Gravitational Search Algorithm, Folded Iterative Chaotic Mapping, Swarm Intelligence, Resilient Stochastic Clustering, Cryptocurrency Historical Prices DatasetAbstract
Cryptocurrency markets have become increasingly complex, making accurate price prediction a challenging task. This article proposes a Hybrid Oppositional Sparrow Search of Gravitational Search Algorithm (HOSS-GSA) which separated iterative chaotic routing to address problems of its probability of falling optimal solutions. The proposed hybrid framework aims to harness the strengths of each component to improve prediction accuracy and capture the dynamics of cryptocurrency historical price data. Resilient Stochastic Clustering effectively identifies relevant features and reduces dimensionality, enhancing the efficiency of subsequent prediction steps. Furthermore, it helps in identifying clusters of similar data patterns within the cryptocurrency historical prices dataset. HOSS-GSA aims to optimize model parameters and improve the overall performance of the prediction model. The experiments were conducted by the common evaluation operations to validate the functionality of grouping for high-dimensional multiview information of Cryptocurrency Historical Prices Dataset, as well as the Wilcoxon rank-sum assessment model was used to measure variable influence for the technique, outperforms traditional prediction methods, achieving higher prediction accuracy and robustness. This approach provides a valuable tool for cryptocurrency traders, investors, and analysts seeking to make informed decisions in a rapidly evolving market.
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