This project demonstrates the application of deep learning techniques for time series forecasting. The goal is to predict future values based on historical data using neural networks.
Time series forecasting is a crucial aspect of many business applications, including stock price prediction, weather forecasting, and demand planning. This project explores the use of deep learning models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), for effective time series forecasting.
deep_learning_for_time_series_forecasting/
│
├── data/
│ ├── train.csv
│ └── test.csv
│
├── deep_learning_for_time_series_forecasting.ipynb
└── README.md
- Clone the repository:
git clone https://github.com/your-username/deep_learning_for_time_series_forecasting.git
- Navigate to the project directory:
cd deep_learning_for_time_series_forecasting - Install the required dependencies:
pip install -r requirements.txt
- Open the Jupyter Notebook:
jupyter notebook deep_learning_for_time_series_forecasting.ipynb
- Follow the steps in the notebook to load the data, preprocess it, define the model, and train it.
The notebook includes several models for time series forecasting:
- Convolutional Neural Network (CNN)
- Long Short-Term Memory Network (LSTM)
- Hybrid CNN-LSTM
The models are built using TensorFlow and Keras, with extensive use of data visualization libraries such as Plotly for interactive plots.
The performance of the models is evaluated using metrics such as Mean Squared Error (MSE). Visualizations of the actual vs. predicted values help in understanding the model's performance.
Contributions are welcome! Please read the contributing guidelines for more information.
This project is licensed under the MIT License. See the LICENSE file for details.