NeuralProphet is an exciting framework designed for human-centered forecasting, combining the power of Neural Networks with traditional time-series algorithms. Imagine being able to quickly build predictive models that not only provide forecasts but also allow for iterative improvements—this is exactly what NeuralProphet aims to deliver!

What You’ll Need

Before diving into NeuralProphet, you’ll need the following:

• Python: Ensure you have Python 3.9 or later installed. For details, visit Python’s official website.
• Pip: You’ll use this package manager to install NeuralProphet.

Installation: Setting Up NeuralProphet

Installing NeuralProphet is a breeze! You can perform the installation using pip. Just follow the steps below:

pip install neuralprophet

If you’re planning to work with Jupyter Notebooks, consider installing the live version to benefit from real-time plotting of training loss:

pip install neuralprophet[live]

And if you want the latest version directly from GitHub, you can clone the repository and install it:

git clone copied link from github
cd neural_prophet
pip install .

**Note**: Windows users should use WSL2 for best compatibility.

Your First Forecasting Model

Let’s get started by building your first model! Here’s a minimal example:

from neuralprophet import NeuralProphet

# Create a model instance
m = NeuralProphet()

# Fit the model with your data
metrics = m.fit(df)

# Generate predictions
forecast = m.predict(df)

# Visualize the results
fig_forecast = m.plot(forecast)
fig_components = m.plot_components(forecast)
fig_model = m.plot_parameters()

How It Works: An Analogy

Think of building a forecasting model with NeuralProphet like preparing a recipe for a delicious dish. You start with a base recipe (the model), which you can adjust by tweaking the ingredients (the parameters) and cooking times (the training iterations). As you taste the dish (evaluate the model), you realize it could use a bit more seasoning (customization) or a longer simmer time (more training data). The beauty of NeuralProphet lies in its iterative nature, allowing you to discover the perfect balance for your desired outcome!

Troubleshooting and Support

Should you encounter any issues or have suggestions, the community is ready to assist. Participate in our discussions on GitHub or join our active Slack community for real-time support.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Final Thoughts

NeuralProphet is an exciting tool that provides both functionality and user-friendliness. Don’t hesitate to explore and alter your model until it meets your forecasting needs. Happy forecasting!