- Converts Tesla stock price data into audio signals using MFCC feature extraction.
- Trains LSTM and SVM models to predict stock price movements based on extracted features.
- Provides a comprehensive analysis of model performance using various metrics.
- Includes both code and datasets required to replicate the study.
- Clone the repository:
git clone https://github.com/jeanqazxcv/master-s-thesis.git
- Navigate to the project directory:
cd master-s-thesis
- Install dependencies:
pip install -r requirements.txt
- Run Jupyter Notebooks:
- Open the Jupyter notebooks in the Code/ directory to start running the experiments. You can use the following command to start Jupyter Notebook:
jupyter notebook
- The following .ipynb files are available:
- mfcc_5daysfeature_tsla_LSTM.ipynb: For training and evaluating the LSTM model.
- mfcc_5daysfeature_tsla_svm.ipynb: For training and evaluating the SVM model.
- mfcc_5daysfeature_tsla_svm_pca.ipynb: For training and evaluating the SVM model with PCA applied to the features.
- Load the Datasets:
- The datasets are located in the Datasets/ directory and include .wav files representing Tesla's stock prices.
- Train the Models:
- Open the respective Jupyter notebooks to preprocess the data, extract MFCC features, and train the models.
- Follow the instructions in each notebook for specific steps.
This project is part of a Master's thesis by Jean Hong, focusing on innovative methods for stock price prediction using audio signal processing techniques.