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https://github.com/gusgitmath/stacked_multi_step_lstm
Project for forecasting Tesla (TSLA) stock prices using advanced LSTM neural networks. Includes a single-step ahead model and a multi-step stacked LSTM model for short and medium-term predictions. Data-driven insights for stock market enthusiasts and practitioners.
https://github.com/gusgitmath/stacked_multi_step_lstm
deep-learning lstm-neural-networks machine-learning neural-network stock-market tensorflow
Last synced: 1 day ago
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Project for forecasting Tesla (TSLA) stock prices using advanced LSTM neural networks. Includes a single-step ahead model and a multi-step stacked LSTM model for short and medium-term predictions. Data-driven insights for stock market enthusiasts and practitioners.
- Host: GitHub
- URL: https://github.com/gusgitmath/stacked_multi_step_lstm
- Owner: GusGitMath
- Created: 2024-08-29T15:33:06.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-08-29T15:33:26.000Z (4 months ago)
- Last Synced: 2025-01-03T14:24:18.341Z (1 day ago)
- Topics: deep-learning, lstm-neural-networks, machine-learning, neural-network, stock-market, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 4.61 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Tesla Stock Forecasting Project
**Please either view the Jupyter notebook in Jupyter Lab or download the Tesla_LSTM.html file to explore the project. This is due to the implementation of interactive plots. Alternatively, you can view the project on my Kaggle page: [Tesla Stock Forecasting | Multi-Step Stacked LSTM](https://www.kaggle.com/code/guslovesmath/tesla-stock-forecasting-multi-step-stacked-lstm)**.
## Overview
This repository contains notebooks and data for a project on forecasting Tesla (TSLA) stock prices using multi-step stacked LSTM neural networks. The project includes a single-step ahead production model using a single LSTM and a multi-stacked LSTM model that predicts multiple days (a month in business days) into the future. The accompanying documentation provides an intuitive and mathematical background on LSTM networks, as well as insights into the use of stacked LSTMs and multistep forecasting for time series analysis.
## Data
- Historical stock price data for Tesla (TSLA).
- Data includes daily closing prices, open, high, low, volume, dividends, and stock splits.
## Models
1. **LSTM Single Step Ahead Model**
- Implements a single LSTM model for one-step ahead stock price forecasting.
- Forecasting two weeks into the future (in business days).
- Data preprocessing, model training, and evaluation.
2. **Multi-Step Stacked LSTM Model**
- Builds a multi-stacked LSTM model for forecasting multiple days into the future.
- Maps n points to m points in the future (a month in business days).
- In-depth analysis of model architecture, training process, and evaluation.
## Files
- **LSTM_Tesla_model.h5:** Trained weights for the single-step ahead LSTM model.
- **Multi_LSTM_Tesla_model.h5:** Trained weights for the multi-step stacked LSTM model.
- **Tesla_LSTM.ipynb:** Jupyter notebook for the project, covering data exploration, model building and implementation, and analysis.
- **Tesla_LSTM.html:** HTML file of the Jupyter notebook for project viewing with interactive plots.
## Dependencies
- Python: 3.11.5
- Libraries:
- yfinance 0.2.32
- pandas 2.1.1
- numpy 1.26.2
- scikit-learn 1.3.1
- plotly 5.18.0
- scipy 1.11.3
- tensorflow 2.14.0
- keras 2.14.0
## Data Sample
```plaintext
open high low close volume dividends stock splits
Date
2023-11-24 233.750 238.750 232.330 235.450 65125200 0.0 0.0
2023-11-27 236.890 238.330 232.100 236.080 112031800 0.0 0.0
2023-11-28 236.680 247.000 234.010 246.720 148549900 0.0 0.0
2023-11-29 249.210 252.750 242.760 244.140 135401300 0.0 0.0
2023-11-30 245.140 245.220 236.910 240.080 132353200 0.0 0.0