README.md

🔮 Predicting the future with NeuralProphet

NeuralProphet is a Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. NeuralProphet bridges the gap between traditional time-series models and deep learning methods.

This example showcases how to train a NeuralProphet model in a ZenML pipeline. The ZenML NeuralProphet integration includes a custom materializer that persists the trained NeuralProphet model to and from the artifact store. Here, we utilize this materializer to train a model to predict the electricity consumption of a hospital.

The data used in this example is available here and the pipeline is loosely based on this guide from the NeuralProphet documentation.

🖥 Run it locally

⏩ SuperQuick neural prophet run

If you're really in a hurry and just want to see this example pipeline run without wanting to fiddle around with all the individual installation and configuration steps, just run the following:

zenml example run neural_prophet

👣 Step-by-Step

📄 Prerequisites

In order to run this example, you need to install and initialize ZenML:

# install CLI
pip install "zenml[server]"

# install ZenML integrations
zenml integration install neural_prophet

# pull example
zenml example pull neural_prophet
cd zenml_examples/neural_prophet

# Initialize ZenML repo
zenml init

# Start the ZenServer to enable dashboard access
zenml up

▶️ Run the Code

Now we're ready. Execute:

python run.py

After running the pipeline, you may inspect the accompanying notebook to visualize results:

jupyter notebook

🧽 Clean up

In order to clean up, delete the remaining ZenML references.

rm -rf zenml_examples