On Multivariate Financial Time Series Classification

2025/04/24 by Grégory Bournassenko, Bournassenko, Grégory
Business, Management and Accounting · Computer Science · Decision Sciences · #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Machine Learning (cs.LG) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2504.17664

openalex publication_date 2025/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
• Evaluating data augmentation for financial time series classification
• Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics
• Deep learning for time series classification: a review
• Distributed and parallel time series feature extraction for industrial big data applications
• Classification-based Financial Markets Prediction using Deep Neural Networks
• Long Short-Term Memory

Related

• Financial Time Series Data Processing for Machine Learning
• Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods
• Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey
• MegazordNet: combining statistical and machine learning standpoints for time series forecasting
• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019