LSTM Stock Price Predictor
| Property | Value |
|---|---|
| Architecture | LSTM (Long Short-Term Memory) |
| Hidden Dimensions | 64 |
| Number of Layers | 5 |
| Training Epochs | 500 |
What is lstm-stock-price-predictor?
The lstm-stock-price-predictor is a specialized deep learning model designed for predicting Google stock prices. Built using PyTorch, this model leverages Long Short-Term Memory (LSTM) architecture to capture temporal dependencies in stock price movements. The model was trained on the Google stock price prediction dataset from Kaggle, demonstrating strong predictive capabilities on both training and test datasets.
Implementation Details
The model implements a deep LSTM architecture with carefully tuned hyperparameters, including an input dimension of 1 (stock price), 64 hidden dimensions across 5 LSTM layers, and an output dimension of 1 for price prediction. The extensive training process spans 500 epochs, optimizing the model's ability to capture both short-term and long-term price patterns.
- 5-layer deep LSTM architecture
- 64 hidden dimensions for robust feature extraction
- Single input and output dimensions for precise price prediction
- Comprehensive training on historical Google stock data
Core Capabilities
- Time series prediction of stock prices
- Pattern recognition in financial data
- Handling of sequential market data
- Real-time price forecasting potential
Frequently Asked Questions
Q: What makes this model unique?
This model's strength lies in its deep architecture with 5 LSTM layers, which allows it to capture complex patterns in stock price movements. The extensive training period of 500 epochs and visualization of training progress demonstrate its optimization for accurate price prediction.
Q: What are the recommended use cases?
The model is specifically designed for Google stock price prediction but can be adapted for similar financial time series prediction tasks. It's suitable for both research purposes and practical applications in financial analysis and algorithmic trading systems.
