# ourownstory/neural_prophet

NeuralProphet: A simple forecasting package

Repository: https://github.com/ourownstory/neural_prophet
Canonical: https://ross.abutalabs.com/products/neural_prophet
Homepage: https://neuralprophet.com
Language: Python
License: MIT
License Family: permissive
Topics: forecasting, time-series, machine-learning, fbprophet, prophet, forecast, artificial-intelligence, prediction, trend, seasonality, autoregression, pytorch, timeseries, forecasting-algorithm, forecasting-model, neuralprophet, neural, neural-network, python, deep-learning
Last push: 2025-01-08T20:48:50+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2312, "days_push": 602, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4295, forks 514 (observed 2026-08-28T04:08:41.502375+00:00)

## What it is
NeuralProphet is a Python library for interpretable time series forecasting built on PyTorch, combining neural networks with traditional time-series algorithms inspired by Facebook Prophet and AR-Net. It supports trends, seasonality, autoregression, regressors, events, and uncertainty quantification with a simple, human-in-the-loop modeling workflow.

## Use cases
- forecast daily sales from historical time series data
- predict energy load or demand for a facility
- model seasonality and trends in sub-daily time series
- migrate an existing Facebook Prophet forecasting workflow to a neural approach
- build interpretable forecasting models with autoregression and lagged regressors
- quantify uncertainty in time series forecasts

## When to choose
- you want an easy-to-learn, interpretable forecasting framework with a Prophet-like API
- your time series is high-frequency (sub-daily) and spans at least two full periods/years
- you want to iterate on model components like trend, seasonality, and events in a human-in-the-loop fashion
- you prefer a PyTorch-based model that integrates with the Python ML ecosystem

## When to avoid
- you need the most accurate out-of-the-box forecasts without tuning or iteration
- your data is low-frequency with only one or fewer observed periods
- you need a fully mature, production-hardened tool - the project notes it is still in beta
- you require non-Python environments or non-PyTorch deep learning backends

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, data-science, time-series, artificial-intelligence
- platform: python
- tags: forecasting, time-series, pytorch, prophet, autoregression, seasonality, interpretability

## Member repositories
- ourownstory/neural_prophet (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:41.502375+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:52.656757+00:00, confidence not recorded.
  - readme: https://github.com/ourownstory/neural_prophet (fetched 2026-08-28T04:08:41.502375+00:00, sha e911c57c80bf)
  - homepage: https://neuralprophet.com (fetched 2026-08-29T09:11:04.821892+00:00, sha 3b5d8f3767d5)
  - site_page: https://neuralprophet.com/contents.html (fetched 2026-08-29T09:11:04.831477+00:00, sha 443feaee748c)
- Data as of 2026-08-30T08:39:29.467469+00:00.