[Paper] Stock Market Price Prediction using Neural Prophet with Deep Neural Network

Source: arXiv - 2601.05202v1

Overview

The paper proposes Neural Prophet with a Deep Neural Network (NP‑DNN) as a new hybrid model for forecasting stock prices. By marrying Facebook’s Neural Prophet time‑series framework with a deep Multi‑Layer Perceptron (MLP), the authors claim to achieve a 99.21 % prediction accuracy, dramatically outperforming baseline statistical methods and a recent fused Large Language Model (LLM) approach.

Key Contributions

• Hybrid Architecture: Combines Neural Prophet’s trend/seasonality handling with a deep MLP to capture nonlinear market dynamics.
• Robust Pre‑processing Pipeline: Uses Z‑score normalization and missing‑value imputation to clean raw price series before modeling.
• Feature Learning: The MLP extracts hidden representations from the Prophet‑generated components, improving forecast fidelity.
• Empirical Benchmarking: Demonstrates superior performance (99.21 % accuracy) against traditional statistical models and a state‑of‑the‑art fused LLM baseline on a publicly available stock dataset.
• Open‑source Reproducibility: The authors release code and configuration files, enabling developers to replicate and extend the experiments.

Methodology

1. Data Collection & Cleaning

   • Historical daily closing prices (open, high, low, close, volume) are gathered for several equities.
   • Missing entries are filled via linear interpolation, then each feature is Z‑score normalized (subtract mean, divide by std) to remove scale bias.

2. Neural Prophet Layer

   • Neural Prophet (an extension of Facebook Prophet) models trend, yearly/weekly seasonality, and holiday effects using a differentiable additive framework.
   • The output is a set of seasonality‑adjusted residuals and trend forecasts, which serve as enriched time‑series features.

3. Deep Neural Network (MLP)

   • A Multi‑Layer Perceptron with three hidden layers (ReLU activations) ingests the Prophet‑derived features plus the original normalized price series.
   • The MLP learns complex, nonlinear interactions that pure Prophet cannot capture (e.g., abrupt market shocks).

4. Training & Evaluation

   • The combined model is trained end‑to‑end using Mean Squared Error (MSE) loss and Adam optimizer.
   • Performance is measured with accuracy, RMSE, and MAE on a held‑out test split (20 % of data).

5. Baseline Comparisons

   • Classical ARIMA, LSTM, and a fused LLM (prompt‑engineered GPT‑4) are re‑implemented under identical data splits for fair comparison.

Results & Findings

• The NP‑DNN reduces prediction error by ~70 % relative to the LSTM baseline.
• Visual inspection of forecast curves shows tighter alignment with actual price movements, especially during volatile periods where pure Prophet or LSTM drift off.
• Ablation studies confirm that both components (Prophet + MLP) are necessary: removing the MLP drops accuracy to ~96 %, while dropping Prophet reduces it to ~93 %.

Practical Implications

• Algorithmic Trading: The high‑precision forecasts can feed into signal generators for short‑term strategies, potentially improving Sharpe ratios when combined with risk controls.
• Portfolio Management: More reliable price outlooks enable better asset allocation and dynamic rebalancing, especially for quantitative funds that rely on daily forecasts.
• FinTech APIs: The modular pipeline (pre‑process → Prophet → MLP) can be wrapped as a micro‑service, allowing developers to plug in custom data sources (e.g., news sentiment) without redesigning the core model.
• Risk & Compliance: Accurate price bands help in stress‑testing and scenario analysis, supporting regulatory reporting that demands forward‑looking risk metrics.
• Edge Deployment: Because the MLP is relatively lightweight (few hundred thousand parameters) and Prophet runs on CPU, the whole stack can be containerized for near‑real‑time inference on cloud or on‑premise servers.

Limitations & Future Work

• Dataset Scope: Experiments are limited to a handful of large‑cap equities; performance on low‑liquidity or crypto assets remains untested.
• Overfitting Risk: Achieving >99 % accuracy on historical data may indicate the model is memorizing patterns that won’t generalize to future market regimes.
• Interpretability: While Prophet offers some explainability (trend/seasonality), the MLP’s internal representations are opaque, which can be a hurdle for compliance‑driven environments.
• Real‑Time Constraints: The current setup assumes daily batch updates; extending to intraday tick data would require latency‑optimized versions of Prophet and the MLP.

Future directions suggested by the authors include:

1. Incorporating alternative data (social media sentiment, macro‑economic indicators) as additional Prophet regressors.
2. Exploring attention‑based architectures (e.g., Temporal Fusion Transformers) to replace or augment the MLP.
3. Conducting out‑of‑sample backtesting across multiple market cycles to assess robustness.
4. Adding explainability layers (SHAP values, counterfactual analysis) to satisfy audit requirements.

Bottom line: NP‑DNN showcases how a thoughtfully combined statistical‑learning pipeline can push stock‑price forecasting accuracy to new heights, offering a practical blueprint for developers building next‑generation fintech analytics tools.

Authors

• Navin Chhibber
• Suneel Khemka
• Navneet Kumar Tyagi
• Rohit Tewari
• Bireswar Banerjee
• Piyush Ranjan

Paper Information

• arXiv ID: 2601.05202v1
• Categories: cs.AI, cs.LG
• Published: January 8, 2026
• PDF: Download PDF