lstm-stock-price-predictor

Maintained By
suryaR-15

lstm-stock-price-predictor

PropertyValue
ArchitectureLSTM (Long Short-Term Memory)
Training DatasetGoogle Stock Price Dataset (Kaggle)
Hidden Dimensions64
Number of Layers5
Training Epochs500

What is lstm-stock-price-predictor?

The lstm-stock-price-predictor is a sophisticated deep learning model designed specifically for predicting Google stock prices. Built using LSTM architecture, this model demonstrates robust capabilities in capturing temporal dependencies in stock price movements. The model was trained on the Google stock price dataset from Kaggle, incorporating 500 epochs of training to achieve optimal performance.

Implementation Details

The model implements a deep LSTM architecture with carefully chosen hyperparameters: 1 input dimension, 64 hidden dimensions across 5 layers, and 1 output dimension. The training process spans 500 epochs, showing consistent improvement in prediction accuracy as evidenced by the training loss curve included in the documentation.

  • Multi-layer LSTM architecture (5 layers)
  • 64 hidden dimensions for robust feature extraction
  • Comprehensive training over 500 epochs
  • Input and output dimensions optimized for stock price prediction

Core Capabilities

  • Accurate prediction of Google stock price movements
  • Effective handling of time-series data
  • Demonstrated performance on both training and test datasets
  • Support for PyTorch-based inference

Frequently Asked Questions

Q: What makes this model unique?

This model combines a deep LSTM architecture with extensive training specifically on Google stock data, making it particularly effective for stock price prediction tasks. The visualization of predicted vs. actual prices demonstrates its practical effectiveness.

Q: What are the recommended use cases?

The model is ideally suited for Google stock price prediction tasks, financial analysis, and as a reference for implementing LSTM-based time series prediction models. It can be particularly valuable for researchers and practitioners in quantitative finance.

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