Awesome
Nixtla Β Β
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section --> <!-- ALL-CONTRIBUTORS-BADGE:END --> <div align="center"> <img src="https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png"> <h1 align="center">Statistical β‘οΈ Forecast</h1> <h3 align="center">Lightning fast forecasting with statistical and econometric models</h3>StatsForecast offers a collection of widely used univariate time series forecasting models, including automatic ARIMA
, ETS
, CES
, and Theta
modeling optimized for high performance using numba
. It also includes a large battery of benchmarking models.
Installation
You can install StatsForecast
with:
pip install statsforecast
or
conda install -c conda-forge statsforecast
Vist our Installation Guide for further instructions.
Quick Start
Minimal Example
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
from statsforecast.utils import AirPassengersDF
df = AirPassengersDF
sf = StatsForecast(
models = [AutoARIMA(season_length = 12)],
freq = 'M'
)
sf.fit(df)
sf.predict(h=12, level=[95])
Get Started with this quick guide.
Follow this end-to-end walkthrough for best practices.
Why?
Current Python alternatives for statistical models are slow, inaccurate and don't scale well. So we created a library that can be used to forecast in production environments or as benchmarks. StatsForecast
includes an extensive battery of models that can efficiently fit millions of time series.
Features
- Fastest and most accurate implementations of
AutoARIMA
,AutoETS
,AutoCES
,MSTL
andTheta
in Python. - Out-of-the-box compatibility with Spark, Dask, and Ray.
- Probabilistic Forecasting and Confidence Intervals.
- Support for exogenous Variables and static covariates.
- Anomaly Detection.
- Familiar sklearn syntax:
.fit
and.predict
.
Highlights
- Inclusion of
exogenous variables
andprediction intervals
for ARIMA. - 20x faster than
pmdarima
. - 1.5x faster than
R
. - 500x faster than
Prophet
. - 4x faster than
statsmodels
. - Compiled to high performance machine code through
numba
. - 1,000,000 series in 30 min with ray.
- Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments here.
- Fit 10 benchmark models on 1,000,000 series in under 5 min.
Missing something? Please open an issue or write us in
Examples and Guides
π End to End Walkthrough: Model training, evaluation and selection for multiple time series
π Anomaly Detection: detect anomalies for time series using in-sample prediction intervals.
π©βπ¬ Cross Validation: robust modelβs performance evaluation.
βοΈ Multiple Seasonalities: how to forecast data with multiple seasonalities using an MSTL.
π Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills.
π Intermittent Demand: forecast series with very few non-zero observations.
π‘οΈ Exogenous Regressors: like weather or prices
Models
Automatic Forecasting
Automatic forecasting tools search for the best parameters and select the best possible model for a group of time series. These tools are useful for large collections of univariate time series.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
AutoARIMA | β | β | β | β | β |
AutoETS | β | β | β | β | |
AutoCES | β | β | β | β | |
AutoTheta | β | β | β | β |
ARIMA Family
These models exploit the existing autocorrelations in the time series.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
ARIMA | β | β | β | β | β |
AutoRegressive | β | β | β | β | β |
Theta Family
Fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
Theta | β | β | β | β | |
OptimizedTheta | β | β | β | β | |
DynamicTheta | β | β | β | β | |
DynamicOptimizedTheta | β | β | β | β |
Multiple Seasonalities
Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
MSTL | β | β | β | β | If trend forecaster supports |
GARCH and ARCH Models
Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
GARCH | β | β | β | β | |
ARCH | β | β | β | β |
Baseline Models
Classical models for establishing baseline.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
HistoricAverage | β | β | β | β | |
Naive | β | β | β | β | |
RandomWalkWithDrift | β | β | β | β | |
SeasonalNaive | β | β | β | β | |
WindowAverage | β | ||||
SeasonalWindowAverage | β |
Exponential Smoothing
Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with clear trend and/or seasonality. Use the SimpleExponential
family for data with no clear trend or seasonality.
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
SimpleExponentialSmoothing | β | ||||
SimpleExponentialSmoothingOptimized | β | ||||
SeasonalExponentialSmoothing | β | ||||
SeasonalExponentialSmoothingOptimized | β | ||||
Holt | β | β | β | β | |
HoltWinters | β | β | β | β |
Sparse or Intermittent
Suited for series with very few non-zero observations
Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
---|---|---|---|---|---|
ADIDA | β | β | β | ||
CrostonClassic | β | β | β | ||
CrostonOptimized | β | β | β | ||
CrostonSBA | β | β | β | ||
IMAPA | β | β | β | ||
TSB | β | β | β |
π¨ How to contribute
See CONTRIBUTING.md.
Citing
@misc{garza2022statsforecast,
author={Azul Garza, Max Mergenthaler Canseco, Cristian ChallΓΊ, Kin G. Olivares},
title = {{StatsForecast}: Lightning fast forecasting with statistical and econometric models},
year={2022},
howpublished={{PyCon} Salt Lake City, Utah, US 2022},
url={https://github.com/Nixtla/statsforecast}
}
Contributors β¨
Thanks goes to these wonderful people (emoji key):
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tbody> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/AzulGarza"><img src="https://avatars.githubusercontent.com/u/10517170?v=4?s=100" width="100px;" alt="azul"/><br /><sub><b>azul</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=AzulGarza" title="Code">π»</a> <a href="#maintenance-AzulGarza" title="Maintenance">π§</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/jmoralez"><img src="https://avatars.githubusercontent.com/u/8473587?v=4?s=100" width="100px;" alt="JosΓ© Morales"/><br /><sub><b>JosΓ© Morales</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=jmoralez" title="Code">π»</a> <a href="#maintenance-jmoralez" title="Maintenance">π§</a></td> <td align="center" valign="top" width="14.28%"><a href="https://www.linkedin.com/in/sugatoray/"><img src="https://avatars.githubusercontent.com/u/10201242?v=4?s=100" width="100px;" alt="Sugato Ray"/><br /><sub><b>Sugato Ray</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=sugatoray" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="http://www.jefftackes.com"><img src="https://avatars.githubusercontent.com/u/9125316?v=4?s=100" width="100px;" alt="Jeff Tackes"/><br /><sub><b>Jeff Tackes</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/issues?q=author%3Atackes" title="Bug reports">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/darinkist"><img src="https://avatars.githubusercontent.com/u/62692170?v=4?s=100" width="100px;" alt="darinkist"/><br /><sub><b>darinkist</b></sub></a><br /><a href="#ideas-darinkist" title="Ideas, Planning, & Feedback">π€</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/alech97"><img src="https://avatars.githubusercontent.com/u/22159405?v=4?s=100" width="100px;" alt="Alec Helyar"/><br /><sub><b>Alec Helyar</b></sub></a><br /><a href="#question-alech97" title="Answering Questions">π¬</a></td> <td align="center" valign="top" width="14.28%"><a href="https://dhirschfeld.github.io"><img src="https://avatars.githubusercontent.com/u/881019?v=4?s=100" width="100px;" alt="Dave Hirschfeld"/><br /><sub><b>Dave Hirschfeld</b></sub></a><br /><a href="#question-dhirschfeld" title="Answering Questions">π¬</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/mergenthaler"><img src="https://avatars.githubusercontent.com/u/4086186?v=4?s=100" width="100px;" alt="mergenthaler"/><br /><sub><b>mergenthaler</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=mergenthaler" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/kdgutier"><img src="https://avatars.githubusercontent.com/u/19935241?v=4?s=100" width="100px;" alt="Kin"/><br /><sub><b>Kin</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=kdgutier" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Yasslight90"><img src="https://avatars.githubusercontent.com/u/58293883?v=4?s=100" width="100px;" alt="Yasslight90"/><br /><sub><b>Yasslight90</b></sub></a><br /><a href="#ideas-Yasslight90" title="Ideas, Planning, & Feedback">π€</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/asinig"><img src="https://avatars.githubusercontent.com/u/99350687?v=4?s=100" width="100px;" alt="asinig"/><br /><sub><b>asinig</b></sub></a><br /><a href="#ideas-asinig" title="Ideas, Planning, & Feedback">π€</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/guerda"><img src="https://avatars.githubusercontent.com/u/230782?v=4?s=100" width="100px;" alt="Philip GilliΓen"/><br /><sub><b>Philip GilliΓen</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=guerda" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/shagn"><img src="https://avatars.githubusercontent.com/u/16029092?v=4?s=100" width="100px;" alt="Sebastian Hagn"/><br /><sub><b>Sebastian Hagn</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/issues?q=author%3Ashagn" title="Bug reports">π</a> <a href="https://github.com/Nixtla/statsforecast/commits?author=shagn" title="Documentation">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/fugue-project/fugue"><img src="https://avatars.githubusercontent.com/u/21092479?v=4?s=100" width="100px;" alt="Han Wang"/><br /><sub><b>Han Wang</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=goodwanghan" title="Code">π»</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://www.linkedin.com/in/benjamin-jeffrey-218548a8/"><img src="https://avatars.githubusercontent.com/u/36240394?v=4?s=100" width="100px;" alt="Ben Jeffrey"/><br /><sub><b>Ben Jeffrey</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/issues?q=author%3Abjeffrey92" title="Bug reports">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Beliavsky"><img src="https://avatars.githubusercontent.com/u/38887928?v=4?s=100" width="100px;" alt="Beliavsky"/><br /><sub><b>Beliavsky</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=Beliavsky" title="Documentation">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/MMenchero"><img src="https://avatars.githubusercontent.com/u/47995617?v=4?s=100" width="100px;" alt="Mariana Menchero GarcΓa "/><br /><sub><b>Mariana Menchero GarcΓa </b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=MMenchero" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://www.linkedin.com/in/guptanick/"><img src="https://avatars.githubusercontent.com/u/33585645?v=4?s=100" width="100px;" alt="Nikhil Gupta"/><br /><sub><b>Nikhil Gupta</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/issues?q=author%3Angupta23" title="Bug reports">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/jdegene"><img src="https://avatars.githubusercontent.com/u/17744939?v=4?s=100" width="100px;" alt="JD"/><br /><sub><b>JD</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/issues?q=author%3Ajdegene" title="Bug reports">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/jattenberg"><img src="https://avatars.githubusercontent.com/u/924185?v=4?s=100" width="100px;" alt="josh attenberg"/><br /><sub><b>josh attenberg</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=jattenberg" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/JeroenPeterBos"><img src="https://avatars.githubusercontent.com/u/15342738?v=4?s=100" width="100px;" alt="JeroenPeterBos"/><br /><sub><b>JeroenPeterBos</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=JeroenPeterBos" title="Code">π»</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/jvdd"><img src="https://avatars.githubusercontent.com/u/18898740?v=4?s=100" width="100px;" alt="Jeroen Van Der Donckt"/><br /><sub><b>Jeroen Van Der Donckt</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=jvdd" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Roymprog"><img src="https://avatars.githubusercontent.com/u/4035367?v=4?s=100" width="100px;" alt="Roymprog"/><br /><sub><b>Roymprog</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=Roymprog" title="Documentation">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/nelsoncardenas"><img src="https://avatars.githubusercontent.com/u/18086414?v=4?s=100" width="100px;" alt="Nelson CΓ‘rdenas BolaΓ±o"/><br /><sub><b>Nelson CΓ‘rdenas BolaΓ±o</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=nelsoncardenas" title="Documentation">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/kschmaus"><img src="https://avatars.githubusercontent.com/u/6586847?v=4?s=100" width="100px;" alt="Kyle Schmaus"/><br /><sub><b>Kyle Schmaus</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=kschmaus" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://www.linkedin.com/in/akmal-soliev/"><img src="https://avatars.githubusercontent.com/u/24494206?v=4?s=100" width="100px;" alt="Akmal Soliev"/><br /><sub><b>Akmal Soliev</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=akmalsoliev" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/nickto"><img src="https://avatars.githubusercontent.com/u/11967792?v=4?s=100" width="100px;" alt="Nick To"/><br /><sub><b>Nick To</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=nickto" title="Code">π»</a></td> <td align="center" valign="top" width="14.28%"><a href="https://www.linkedin.com/in/kvnkho/"><img src="https://avatars.githubusercontent.com/u/32503212?v=4?s=100" width="100px;" alt="Kevin Kho"/><br /><sub><b>Kevin Kho</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=kvnkho" title="Code">π»</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/yibenhuang"><img src="https://avatars.githubusercontent.com/u/62163340?v=4?s=100" width="100px;" alt="Yiben Huang"/><br /><sub><b>Yiben Huang</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=yibenhuang" title="Documentation">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/andrewgross"><img src="https://avatars.githubusercontent.com/u/370118?v=4?s=100" width="100px;" alt="Andrew Gross"/><br /><sub><b>Andrew Gross</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=andrewgross" title="Documentation">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/taniishkaaa"><img src="https://avatars.githubusercontent.com/u/109246904?v=4?s=100" width="100px;" alt="taniishkaaa"/><br /><sub><b>taniishkaaa</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=taniishkaaa" title="Documentation">π</a></td> <td align="center" valign="top" width="14.28%"><a href="https://manuel.calzolari.name"><img src="https://avatars.githubusercontent.com/u/2764902?v=4?s=100" width="100px;" alt="Manuel Calzolari"/><br /><sub><b>Manuel Calzolari</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=manuel-calzolari" title="Code">π»</a></td> </tr> </tbody> </table> <!-- markdownlint-restore --> <!-- prettier-ignore-end --> <!-- ALL-CONTRIBUTORS-LIST:END -->This project follows the all-contributors specification. Contributions of any kind welcome!