Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data

ArXiv ID: 2411.12753 “View on arXiv”

Authors: Unknown

Abstract

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy performance. Using the Sharpe and Information Ratios, it specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns. The study focuses on Bitcoin, Litecoin, and Ethereum as the traded assets from January 1, 2016, 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.

Keywords: Supervised Autoencoders, Noise Augmentation, Triple Barrier Labeling, Sharpe Ratio, Time Series Forecasting, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts such as fractional differentiation, binomial series expansions, and statistical stationarity tests, while also featuring a rigorous empirical design with detailed backtesting methodology, risk-adjusted metrics, and specific algorithmic implementations.

  flowchart TD
    A["Research Goal: Enhance financial time series forecasting"] --> B["Data Collection<br>Bitcoin, Litecoin, Ethereum<br>Jan 2016 - Apr 2022"]
    B --> C["Preprocessing<br>Fractional Differentiation & Noise Augmentation"]
    C --> D["Labeling<br>Triple Barrier Method"]
    D --> E["Modeling<br>Supervised Autoencoders<br>Bottleneck Size Optimization"]
    E --> F["Performance Evaluation<br>Sharpe & Information Ratios"]
    F --> G["Key Findings<br>Balanced noise & bottleneck size improve returns<br>Excessive noise impairs performance"]