Neural Prophet

Abstract

1. Introduction

NeuralProphet is an open-source community project, supported by awesome people like you. If you are interested in joining the project, please feel free to reach out to me (Oskar) - you can find my email on the NeuralProphet Paper.

NeuralProphet is an easy to learn framework for interpretable time series forecasting. NeuralProphet is built on PyTorch and combines Neural Networks and traditional time-series algorithms, inspired by Facebook Prophet and AR-Net.

The documentation page may not be entirely up to date. Docstrings should be reliable, please refer to those when in doubt. We are working on an improved documentation. We appreciate any help to improve and update the docs.

2. Objective

NeuralProphet: A simple forecasting package

This project demonstrates how Python, Machine Learning can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Multiple time series: Fit a global/glocal model with (partially) shared model parameters.
• Uncertainty: Estimate values of specific quantiles - Quantile Regression.
• Regularize modelling components.
• Plotting of forecast components, model coefficients and more.
• Time series crossvalidation utility.
• Model checkpointing and validation.

4. Technology Stack

• Autoregression: Autocorrelation modelling - linear or NN (AR-Net).
• Trend: Piecewise linear trend with optional automatic changepoint detection.
• Seasonality: Fourier terms at different periods such as yearly, daily, weekly, hourly.
• Lagged regressors: Lagged observations (e.g temperature sensor) - linear or NN.
• Future regressors: In advance known features (e.g. temperature forecast) - linear or NN.
• Events: Country holidays & recurring custom events.
• Global Modeling: Components can be local, global or 'glocal' (global + regularized local)
• Multiple time series: Fit a global/glocal model with (partially) shared model parameters.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/ourownstory/neural_prophet.git
cd neural_prophet

pip install neuralprophet

pip install neuralprophet[live]

git clone <copied link from github>
cd neural_prophet
pip install .

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by ourownstory and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.