Stock Price Predictor: Leveraging historical data, macroeconomic indicators, LSTM and Prophet models for enhanced stock price forecasting. Analyze trends, patterns, and economic factors to gain insights and make data-driven predictions. Leverage advanced modeling techniques for reliable forecasts.
- Historical Data Analysis: Gain insights from historical stock price data to identify trends, patterns, and correlations.
- Macroeconomic Indicators: Incorporate macroeconomic indicators to enhance the predictive capabilities of the models.
- LSTM Model: Utilize the LSTM model to capture long-term dependencies in stock price data and make accurate predictions.
- Prophet Model: Leverage the Prophet model for time series forecasting, taking into account seasonality and trends.
- Evaluation Metrics: Evaluate the performance of the models using metrics such as mean squared error (MSE) and mean absolute error (MAE).
The project uses historical stock price data of Apple Inc. (AAPL) as an example, but it can be easily adapted to any other stock. The dataset includes daily stock prices, volume, and other relevant attributes. You can replace the dataset with your desired stock data to perform analysis and predictions.
- Clone the repository:
- Install the required dependencies:
- Prepare the data:
- Update the macroeconomic indicators dataset if necessary.
- Run the notebook:
- Open the
stock_price_prediction.ipynbnotebook. - Follow the instructions provided in the notebook to preprocess the data, train the models, and make predictions specific to Apple Inc.
The Stock Price Predictor project has demonstrated promising results in accurately forecasting stock prices for Apple Inc. The models have been trained and evaluated using historical data specifically for Apple Inc. and have shown consistent performance in predicting its stock prices.
Contributions to the project are welcome! If you'd like to contribute, please follow these steps:
- Fork the repository on GitHub.
- Create a new branch from the 'main' branch to work on your changes.
- Make your modifications and commit your changes.
- Push your changes to your forked repository.
- Open a pull request on the main repository to submit your changes for review.
Please ensure that your contributions align with the project's coding style and guidelines.
This project is licensed under the MIT License. See the LICENSE file for more information.
We would like to acknowledge the following:
- FRED (Federal Reserve Economic Data): We utilized macroeconomic indicators from FRED, which provided valuable data to enhance the predictive capabilities of our models.
- Facebook Prophet Team: We would like to express our gratitude to the team at Facebook responsible for developing Prophet. Their open-source time series forecasting library has been instrumental in our project.
We are grateful for their contributions and the resources they have provided, which greatly assisted in the development and success of this project.
- Robert Rusev - robertrusev
For any inquiries or suggestions, please contact me at robert.rusev@yahoo.com .