NeuralProphet: Explainable Forecasting at Scale

TL;DR

NeuralProphet is a scalable, explainable forecasting framework that enhances Prophet with local context modeling using deep learning, significantly improving short to medium-term forecast accuracy on real-world datasets.

Contribution

NeuralProphet introduces a hybrid deep learning-based forecasting framework that extends Prophet with local context modeling, improving accuracy and extensibility.

Findings

NeuralProphet produces interpretable components comparable or superior to Prophet.

It outperforms Prophet on diverse real-world datasets.

Forecast accuracy improves by 55 to 92 percent for short to medium-term predictions.

Abstract

We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferation of time series data, explainable forecasting remains a challenging task for business and operational decision making. Hybrid solutions are needed to bridge the gap between interpretable classical methods and scalable deep learning models. We view Prophet as a precursor to such a solution. However, Prophet lacks local context, which is essential for forecasting the near-term future and is challenging to extend due to its Stan backend. NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework. Local context is introduced with auto-regression and covariate modules, which can be configured as classical linear regression or as Neural Networks. Otherwise, NeuralProphet retains the design philosophy of Prophet and provides the same basic model components. Our results demonstrate that NeuralProphet produces interpretable forecast components of equivalent or superior quality to Prophet on a set of generated time series. NeuralProphet outperforms Prophet on a diverse collection of real-world datasets. For short to medium-term forecasts, NeuralProphet improves forecast accuracy by 55 to 92 percent.