# Supervised Autoencoder MLP for Financial Time Series Forecasting

arXiv: https://arxiv.org/abs/2404.01866 · HTML: https://mlsanity.com/paper/2404.01866/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Bitcoin Price Prediction: An ARIMA Approach](https://mlsanity.com/paper/1904.05315/) (2019)

## Built on by (most cited first, 2 of 2)

- [Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market](https://mlsanity.com/paper/2406.10695/) (2024)
- [Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting](https://mlsanity.com/paper/2606.00060/) (2026)

## Abstract (arXiv)

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2010, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors.