arXiv subjects

Grégory Bournassenko

Publications and source records attributed to Grégory Bournassenko.

3 recordsLinked to original sources

arXiv2025

On Multivariate Financial Time Series Classification

This article investigates the use of Machine Learning and Deep Learning models in multivariate time series analysis within financial markets. It compares small and big data approaches, focusing on their distinct challenges and the benefits of scaling. Traditional methods such as SVMs are contrasted with modern architectures like ConvTimeNet. The results show the importance of using and understanding Big Data in depth in the analysis and prediction of financial time series.

cs.LG↗

arXiv2025

On Bitcoin Price Prediction

In recent years, cryptocurrencies have attracted growing attention from both private investors and institutions. Among them, Bitcoin stands out for its impressive volatility and widespread influence. This paper explores the predictability of Bitcoin's price movements, drawing a parallel with traditional financial markets. We examine whether the cryptocurrency market operates under the efficient market hypothesis (EMH) or if inefficiencies still allow opportunities for arbitrage. Our methodology combines theoretical reviews, empirical analyses, machine learning approaches, and time series modeling to assess the extent to which Bitcoin's price can be predicted. We find that while, in general, the Bitcoin market tends toward efficiency, specific conditions, including information asymmetries and behavioral anomalies, occasionally create exploitable inefficiencies. However, these opportunities remain difficult to systematically identify and leverage. Our findings have implications for both investors and policymakers, particularly regarding the regulation of cryptocurrency brokers and derivatives markets.

q-fin.ST↗

arXiv2025

On Queueing Theory for Large-Scale CI/CD Pipelines Optimization

Continuous Integration and Continuous Deployment (CI/CD) pipelines are central to modern software development. In large organizations, the high volume of builds and tests creates bottlenecks, especially under shared infrastructure. This article proposes a modeling framework based on queueing theory to optimize large-scale CI/CD workflows. We formalize the system using classical $M/M/c$ queueing models and discuss strategies to minimize delays and infrastructure costs. Our approach integrates theoretical results with practical techniques, including dynamic scaling and prioritization of CI/CD tasks.

cs.SE↗