The project aims to use time-series machine learning forecasting models to predict stock prices.
The project aims to solve the problem of predicting stock prices using time-series machine learning models. The goal is to develop models that can accurately forecast the future price of a stock based on its historical price data.
The dataset used for the project consists of historical stock price data for various companies. The data includes features such as opening price, closing price, volume, and other financial indicators.
The project uses time-series forecasting models such as Theta model, Exponential Moving Average, ARIMA, Prophet, and LSTM to predict the future price of a stock based on its historical price data. The models are trained and tested using a time series split and evaluated using mean absolute percentage error (MAPE) as the metric.
The project has achieved promising results in predicting stock prices using the developed time-series forecasting models.
| Model | Performance (MAPE) (%) |
|---|---|
| ARIMA | 3.7088 |
| Ensemble model | 1.3429 |
| Prophet | 2.4516 |
| LSTM | 8.747 |
Future work includes exploring the use of other time-series forecasting models and improving the accuracy of the current models.
The project is implemented in Python and requires the following libraries:
- pandas
- numpy
- scikit-learn
- statsmodels
- fbprophet
- tensorflow
- pytorch
This application is also deployed to a web app written in Flask. To run the web app, follow these steps:
- Install the required libraries by running
pip sync requirements.txt. - Run the Flask app by executing
python server/app.py. - Navigate to
http://localhost:5000in your web browser to access the web app.
If you'd like to contribute to the project, feel free to submit a pull request. We welcome all contributions!
This project is licensed under the MIT License - see the LICENSE file for details.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent