Universal LSTM Stock Prediction Model

Model Description

This is a Universal LSTM model trained for stock price prediction with sentiment analysis integration. The model can predict stock prices for multiple stocks using technical indicators and sentiment features.

Model Details

Model Type: LSTM (Long Short-Term Memory) Neural Network
Framework: TensorFlow/Keras
Task: Time Series Prediction / Stock Price Forecasting
Training Date: July 5, 2025
Input Features: Technical indicators + Sentiment scores
Output: Next-day stock price prediction

Architecture

Input Shape: (None, 60, 6)
Output Shape: (None, 1)
Parameters: 31,651

The model uses a multi-layer LSTM architecture optimized for financial time series prediction.

Training Data

Stocks: Multiple stocks including AAPL, MSFT, GOOGL, AMZN, TSLA, META, NVDA
Features:
- Technical indicators (Open, High, Low, Close, Volume)
- Sentiment scores from financial news analysis
- Moving averages and other derived features
Time Period: Historical stock data with corresponding news sentiment
Preprocessing: StandardScaler normalization

Performance

The model has been evaluated on multiple stocks with varying performance metrics. Best performance achieved:

RMSE: Varies by stock (typically 5-15 points)
Architecture: Optimized through hyperparameter tuning

Usage

import tensorflow as tf
import pickle
import numpy as np

# Load the model
model = tf.keras.models.load_model('stage2_universal_lstm_20250705_170829.keras')

# Load the scaler
with open('stage2_scalers_20250705_170829.pkl', 'rb') as f:
    scalers = pickle.load(f)

# Prepare your data (X should be scaled using the same scaler)
# X = your_data  # Shape: (batch_size, sequence_length, features)
# predictions = model.predict(X)

Files Included

stage2_universal_lstm_20250705_170829.keras: Main model file
stage2_scalers_20250705_170829.pkl: Feature scalers for preprocessing
stage2_metadata_20250705_170829.json: Model metadata and configuration
stage2_architecture_20250705_170829.txt: Detailed architecture description

Citation

If you use this model in your research, please cite:

@misc{lstm_stock_prediction_2025,
  title={Universal LSTM Stock Prediction Model with Sentiment Analysis},
  author={Jeng Yang},
  year={2025},
  url={https://huggingface.co/jengyang/lstm-stock-prediction-model}
}

Disclaimer

This model is for research and educational purposes only. Stock market predictions are inherently uncertain and this model should not be used as the sole basis for financial decisions. Always consult with financial professionals and conduct your own research before making investment decisions.

Contact

For questions or issues, please open an issue in the model repository.