High-frequency forecasting of the crude oil futures price with multiple timeframe predictions fusion

Zhu, Yingke; Duan, Shuangyang; Yu, Yiting; Fu, Zhe; Liu, Jiahe; Yang, Xinling; Liu, Zonghua

doi:10.1016/j.eswa.2023.119580

Cited by 13 publications

(1 citation statement)

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“…Furthermore, incorporating Bayesian Optimization allows automatic tuning of hyperparameters, resulting in improved model performance and e ciency. Bayesian Optimization is particularly well-suited for optimizing complex, high-dimensional search spaces, making it an ideal choice for ne-tuning the parameters of the Bi-LSTM CNN architecture (Deng et al, 2024) (Khan et al, 2023). One of the key highlights of our approach lies in integrating a diverse set of machine learning (ML) and deep learning algorithms to assess the performance of the proposed model.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

Priyadarshini

2024

Preprint

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Monitoring space debris is vital for ensuring the safety of space missions and satellite operations amid the increasing number of satellites and spacecraft in orbit. The study addresses this challenge by proposing a novel approach based on a hybrid Bi-LSTM-CNN architecture optimized using Bayesian Optimization. Through extensive analysis utilizing machine learning and deep learning techniques, the study develops a robust space debris detection system capable of classifying both the object type and Radar Cross Section (RCS) size. The proposed method outperforms existing approaches by demonstrating superior performance across multiple evaluation metrics, including accuracy, precision, recall, and F1 score. Moreover, the study considers the practical aspect of training time, ensuring efficiency in real-time applications. Empirical validation on real-world datasets confirms the effectiveness and efficiency of the hybrid model in accurately detecting and predicting space debris types. Overall, this research significantly advances space debris monitoring capabilities, mitigating risks associated with space exploration and satellite operations, and offers comprehensive insights into potential hazards and optimizing mitigation strategies.

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Section: Introductionmentioning

confidence: 99%