Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS

ArXiv ID: 2409.00480 “View on arXiv”

Authors: Unknown

Abstract

In the rapidly evolving field of financial forecasting, the application of neural networks presents a compelling advancement over traditional statistical models. This research paper explores the effectiveness of two specific neural forecasting models, N-HiTS and N-BEATS, in predicting financial market trends. Through a systematic comparison with conventional models, this study demonstrates the superior predictive capabilities of neural approaches, particularly in handling the non-linear dynamics and complex patterns inherent in financial time series data. The results indicate that N-HiTS and N-BEATS not only enhance the accuracy of forecasts but also boost the robustness and adaptability of financial predictions, offering substantial advantages in environments that require real-time decision-making. The paper concludes with insights into the practical implications of neural forecasting in financial markets and recommendations for future research directions.

Keywords: Neural networks, N-HiTS, N-BEATS, Financial forecasting, Time series prediction

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper includes advanced neural network architectures and mathematical formulations like basis expansion and time series decomposition, showing moderate-to-high mathematical density. It demonstrates empirical rigor by using real financial data from Yahoo Finance, implementing models in Python with specific libraries, and reporting comprehensive error metrics (MAE, RMSE, MAPE, SMAPE) for backtest-ready evaluation.

  flowchart TD
    A["Research Goal: Compare Neural Models N-HiTS & N-BEATS vs. Traditional Models for Financial Forecasting"] --> B{"Data Input: Financial Time Series Data"}
    B --> C["Preprocessing & Splitting"]
    C --> D["Model Training"]
    subgraph D ["Computational Processes"]
        D1["N-HiTS Model"]
        D2["N-BEATS Model"]
        D3["Traditional Statistical Models"]
    end
    D --> E["Forecasting & Evaluation"]
    E --> F{"Key Findings & Outcomes"}
    F --> G["Neural Models Superior Accuracy"]
    F --> H["Enhanced Robustness & Adaptability"]
    F --> I["Better Handling of Non-Linear Dynamics"]