# On Multivariate Financial Time Series Classification

arXiv: https://arxiv.org/abs/2504.17664 · HTML: https://mlsanity.com/paper/2504.17664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Distributed and parallel time series feature extraction for industrial big data applications](https://mlsanity.com/paper/1610.07717/) (2016)
- [ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis](https://mlsanity.com/paper/2403.01493/) (2024)
- [Classification-based Financial Markets Prediction using Deep Neural Networks](https://mlsanity.com/paper/1603.08604/) (2016)
- [Evaluating data augmentation for financial time series classification](https://mlsanity.com/paper/2010.15111/) (2020)
- [Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics](https://mlsanity.com/paper/1908.04383/) (2019)

## Abstract (arXiv)

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.