Supervised autoencoder MLP for financial time series forecasting

Supervised autoencoder MLP for financial time series forecasting

Abstract 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-adju...

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Bibliographic Details
Main Authors: Bartosz Bieganowski, Robert Ślepaczuk
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:Journal of Big Data
Subjects:
Machine learning
Algorithmic investment strategy
Supervised autoencoders
Financial time series
Trading strategy
Risk-adjusted return
Online Access:https://doi.org/10.1186/s40537-025-01267-7
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https://doi.org/10.1186/s40537-025-01267-7

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