# NeuralProphet: Explainable Forecasting at Scale

arXiv: https://arxiv.org/abs/2111.15397 · HTML: https://mlsanity.com/paper/2111.15397/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://mlsanity.com/paper/1708.07120/) (2017)
- [AR-Net: A simple Auto-Regressive Neural Network for time-series](https://mlsanity.com/paper/1911.12436/) (2019)

## Built on by (most cited first, 22 of 22)

- [Forecasting Future World Events with Neural Networks](https://mlsanity.com/paper/2206.15474/) (2022)
- [Learning Fast and Slow for Online Time Series Forecasting](https://mlsanity.com/paper/2202.11672/) (2022)
- [From Similarity to Superiority: Channel Clustering for Time Series Forecasting](https://mlsanity.com/paper/2404.01340/) (2024)
- [Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models](https://mlsanity.com/paper/2305.11475/) (2023)
- [DAM: Towards A Foundation Model for Time Series Forecasting](https://mlsanity.com/paper/2407.17880/) (2024)
- [Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks](https://mlsanity.com/paper/2202.12441/) (2022)
- [Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning](https://mlsanity.com/paper/2306.09290/) (2023)
- [Neural Additive Models for Nowcasting](https://mlsanity.com/paper/2205.10020/) (2022)
- [Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transform](https://mlsanity.com/paper/2405.13812/) (2024)
- [DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting](https://mlsanity.com/paper/2405.00522/) (2024)
- [Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition](https://mlsanity.com/paper/2603.13702/) (2026)
- [Experimental study of time series forecasting methods for groundwater level prediction](https://mlsanity.com/paper/2209.13927/) (2022)
- [Membership Inference Attacks Against Time-Series Models](https://mlsanity.com/paper/2407.02870/) (2024)
- [GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning](https://mlsanity.com/paper/2410.04560/) (2024)
- [Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting](https://mlsanity.com/paper/2212.06620/) (2022)
- [QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting](https://mlsanity.com/paper/2306.05989/) (2023)
- [Neural Network Modeling for Forecasting Tourism Demand in Stopi'ca Cave: A Serbian Cave Tourism Study](https://mlsanity.com/paper/2404.04974/) (2024)
- [Designing forecasting software for forecast users: Empowering non-experts to create and understand their own forecasts](https://mlsanity.com/paper/2404.14575/) (2024)
- [A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural Communities](https://mlsanity.com/paper/2407.15865/) (2024)
- [Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data](https://mlsanity.com/paper/2508.15369/) (2025)
- [Extending Load Forecasting from Zonal Aggregates to Individual Nodes for Transmission System Operators](https://mlsanity.com/paper/2510.14983/) (2025)
- [Using LLMs for Explainable, Data-Driven Insight Generation from Time Series](https://mlsanity.com/paper/2607.18271/) (2026)

## Abstract (arXiv)

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.