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.
View on arXiv@article{bournassenko2025_2504.17664,
title={ On Multivariate Financial Time Series Classification },
author={ Grégory Bournassenko },
journal={arXiv preprint arXiv:2504.17664},
year={ 2025 }
}