# Evaluating data augmentation for financial time series classification

arXiv: https://arxiv.org/abs/2010.15111 · HTML: https://mlsanity.com/paper/2010.15111/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation](https://mlsanity.com/paper/2102.08310/) (2021)
- [StyleTime: Style Transfer for Synthetic Time Series Generation](https://mlsanity.com/paper/2209.11306/) (2022)
- [LLT: An R package for Linear Law-based Feature Space Transformation](https://mlsanity.com/paper/2304.14211/) (2023)
- [Augment on Manifold: Mixup Regularization with UMAP](https://mlsanity.com/paper/2312.13141/) (2023)
- [Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction](https://mlsanity.com/paper/2106.04114/) (2021)
- [Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data](https://mlsanity.com/paper/2411.12753/) (2024)
- [Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates](https://mlsanity.com/paper/2504.03761/) (2025)
- [On Multivariate Financial Time Series Classification](https://mlsanity.com/paper/2504.17664/) (2025)
- [Financial time series augmentation using transformer based GAN architecture](https://mlsanity.com/paper/2602.17865/) (2026)

## Abstract (arXiv)

Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage. This is even more so in the field of financial prediction, where data tends to be small, noisy and non-stationary. In this paper we evaluate several augmentation methods applied to stocks datasets using two state-of-the-art deep learning models. The results show that several augmentation methods significantly improve financial performance when used in combination with a trading strategy. For a relatively small dataset (≈30K samples), augmentation methods achieve up to 400% improvement in risk adjusted return performance; for a larger stock dataset (≈300K samples), results show up to 40% improvement.