On Multivariate Financial Time Series Classification
By: Grégory Bournassenko
Potential Business Impact:
Predicts stock prices better using big computer data.
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.
Similar Papers
Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques
Machine Learning (CS)
Helps predict future events from past data.
Deep Learning for Time Series Forecasting: A Survey
Machine Learning (CS)
Makes computer predictions of future events more accurate.
Multi-modal Time Series Analysis: A Tutorial and Survey
Machine Learning (CS)
Combines different data types to understand changes over time.