RNN Hyperparameter Tuning to Forecast Daily MW with 30-day Gap

A compact and informative demonstration to show how to forecast a time series with a 30-day gap using a double-stacked GRU model, built via the tensorflow library. This notebook demonstrates the use of the lookback window size, dropout rate, and recurrent dropout rate as hyperparameters for tuning.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed the latest version of Conda.

Installation

To install this project, follow these steps:

conda env create -f environment.yml -n custom_env_name

This command will create a new Conda environment that includes the dependencies needed for the project.

Usage

To use this project, follow these steps:

conda activate <env_name>
jupyter notebook

Replace <env_name> with the name of the Conda environment specified above. This will activate the environment and start Jupyter Notebook, where you can open and run the notebook file.

Contributors

The following individuals have contributed to this project:

• Ian CoKehyeng