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https://github.com/Nixtla/statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.
https://github.com/Nixtla/statsforecast

arima automl baselines data-science econometrics ets exponential-smoothing fbprophet forecasting machine-learning mstl naive neuralprophet predictions prophet python seasonal-naive statistics theta time-series

Last synced: 2 months ago
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Lightning ⚡️ fast forecasting with statistical and econometric models.

Host: GitHub
URL: https://github.com/Nixtla/statsforecast
Owner: Nixtla
License: apache-2.0
Created: 2021-11-24T02:19:14.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-05-21T18:41:24.000Z (8 months ago)
Last Synced: 2024-05-22T09:42:50.518Z (8 months ago)
Topics: arima, automl, baselines, data-science, econometrics, ets, exponential-smoothing, fbprophet, forecasting, machine-learning, mstl, naive, neuralprophet, predictions, prophet, python, seasonal-naive, statistics, theta, time-series
Language: Python
Homepage: https://nixtlaverse.nixtla.io/statsforecast
Size: 178 MB
Stars: 3,600
Watchers: 36
Forks: 247
Open Issues: 97
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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Awesome-time-series - 📚 StatsForecast - | ⭐️ 2.2K | 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. (Others)
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awesome-marketing-machine-learning - statsforecast
awesome-list - StatsForecast - Offers a collection of widely used univariate time series forecasting models, including automatic ARIMA and ETS modeling optimized for high performance using numba. (Time-Series & Financial / Others)
StarryDivineSky - Nixtla/statsforecast

README

        # Nixtla   [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Statistical%20Forecasting%20Algorithms%20by%20Nixtla%20&url=https://github.com/Nixtla/statsforecast&via=nixtlainc&hashtags=StatisticalModels,TimeSeries,Forecasting)  [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ)

[![All Contributors](https://img.shields.io/badge/all_contributors-32-orange.svg?style=flat-square)](#contributors-)





Statistical ⚡️ Forecast

Lightning fast forecasting with statistical and econometric models

    

[![CI](https://github.com/Nixtla/statsforecast/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/Nixtla/statsforecast/actions/workflows/ci.yaml)

[![Python](https://img.shields.io/pypi/pyversions/statsforecast)](https://pypi.org/project/statsforecast/)

[![PyPi](https://img.shields.io/pypi/v/statsforecast?color=blue)](https://pypi.org/project/statsforecast/)

[![conda-nixtla](https://img.shields.io/conda/vn/conda-forge/statsforecast?color=seagreen&label=conda)](https://anaconda.org/conda-forge/statsforecast)

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/Nixtla/statsforecast/blob/main/LICENSE)

[![docs](https://img.shields.io/website-up-down-green-red/http/nixtla.github.io/statsforecast.svg?label=docs)](https://nixtla.github.io/statsforecast/)

[![Downloads](https://pepy.tech/badge/statsforecast)](https://pepy.tech/project/statsforecast)

    

**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:

```python

pip install statsforecast

```

or 

```python

conda install -c conda-forge statsforecast

``` 

Vist our [Installation Guide](https://nixtla.github.io/statsforecast/docs/getting-started/installation.html) for further instructions.

## Quick Start

**Minimal Example**

```python

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](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_short.html).**

**Follow this [end-to-end walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html) 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` and `Theta` 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` and `prediction intervals` for ARIMA.

* 20x [faster](./experiments/arima/) than `pmdarima`.

* 1.5x faster than `R`.

* 500x faster than `Prophet`. 

* 4x [faster](./experiments/ets/) than `statsmodels`.

* Compiled to high performance machine code through [`numba`](https://numba.pydata.org/).

* 1,000,000 series in [30 min](https://github.com/Nixtla/statsforecast/tree/main/experiments/ray) with [ray](https://github.com/ray-project/ray).

* Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments [here](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter).

* Fit 10 benchmark models on **1,000,000** series in [under **5 min**](./experiments/benchmarks_at_scale/). 

Missing something? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A)

## Examples and Guides

📚 [End to End Walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html): Model training, evaluation and selection for multiple time series

🔎 [Anomaly Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals.

👩‍🔬 [Cross Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): robust model’s performance evaluation.

❄️ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL.

🔌 [Predict Demand Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills.

📈 [Intermittent Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations. 

🌡️ [Exogenous Regressors](https://nixtla.github.io/statsforecast/docs/how-to-guides/exogenous.html): 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](https://nixtla.github.io/statsforecast/src/core/models.html#autoarima)|✅|✅|✅|✅|✅|

|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|✅|✅|✅|✅||

|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|✅|✅|✅|✅||

|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta)|✅|✅|✅|✅||

### 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](https://nixtla.github.io/statsforecast/src/core/models.html#arima)|✅|✅|✅|✅|✅|

|[AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#autoregressive)|✅|✅|✅|✅|✅|

### 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](https://nixtla.github.io/statsforecast/src/core/models.html#theta)|✅|✅|✅|✅||

|[OptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#optimizedtheta)|✅|✅|✅|✅||

|[DynamicTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamictheta)|✅|✅|✅|✅||

|[DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamicoptimizedtheta)|✅|✅|✅|✅||

### 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](https://nixtla.github.io/statsforecast/src/core/models.html#mstl)|✅|✅|✅|✅|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](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|✅|✅|✅|✅||

|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch)|✅|✅|✅|✅||

### Baseline Models

Classical models for establishing baseline.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|

|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|

|[HistoricAverage](https://nixtla.github.io/statsforecast/src/core/models.html#historicaverage)|✅|✅|✅|✅||

|[Naive](https://nixtla.github.io/statsforecast/src/core/models.html#naive)|✅|✅|✅|✅||

|[RandomWalkWithDrift](https://nixtla.github.io/statsforecast/src/core/models.html#randomwalkwithdrift)|✅|✅|✅|✅||

|[SeasonalNaive](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalnaive)|✅|✅|✅|✅||

|[WindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#windowaverage)|✅|||||

|[SeasonalWindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalwindowaverage)|✅|||||

### 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](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)|✅|||||

|[SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)|✅|||||

|[SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)|✅|||||

|[SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized)|✅|||||

|[Holt](https://nixtla.github.io/statsforecast/src/core/models.html#holt)|✅|✅|✅|✅||

|[HoltWinters](https://nixtla.github.io/statsforecast/src/core/models.html#holtwinters)|✅|✅|✅|✅||

### 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](https://nixtla.github.io/statsforecast/src/core/models.html#adida)|✅||✅|✅||

|[CrostonClassic](https://nixtla.github.io/statsforecast/src/core/models.html#crostonclassic)|✅||✅|✅||

|[CrostonOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#crostonoptimized)|✅||✅|✅||

|[CrostonSBA](https://nixtla.github.io/statsforecast/src/core/models.html#crostonsba)|✅||✅|✅||

|[IMAPA](https://nixtla.github.io/statsforecast/src/core/models.html#imapa)|✅||✅|✅||

|[TSB](https://nixtla.github.io/statsforecast/src/core/models.html#tsb)|✅||✅|✅||

## 🔨 How to contribute

See [CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md).

## Citing

```bibtex

@misc{garza2022statsforecast,

    author={Azul Garza, Max Mergenthaler Canseco, Cristian Challú, 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 ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

  

    

      
_azul
💻 🚧

      
_{José Morales}
💻 🚧

      
_{Sugato Ray}
💻

      
_{Jeff Tackes}
🐛

      
_darinkist
🤔

      
_{Alec Helyar}
💬

      
_{Dave Hirschfeld}
💬

    

    

      
_mergenthaler
💻

      
_Kin
💻

      
_Yasslight90
🤔

      
_asinig
🤔

      
_{Philip Gillißen}
💻

      
_{Sebastian Hagn}
🐛 📖

      
_{Han Wang}
💻

    

    

      
_{Ben Jeffrey}
🐛

      
_Beliavsky
📖

      
_{Mariana Menchero García}
💻

      
_{Nikhil Gupta}
🐛

      
_JD
🐛

      
_{josh attenberg}
💻

      
_{JeroenPeterBos}
💻

    

    

      
_{Jeroen Van Der Donckt}
💻

      
_Roymprog
📖

      
_{Nelson Cárdenas Bolaño}
📖

      
_{Kyle Schmaus}
💻

      
_{Akmal Soliev}
💻

      
_{Nick To}
💻

      
_{Kevin Kho}
💻

    

    

      
_{Yiben Huang}
📖

      
_{Andrew Gross}
📖

      
_taniishkaaa
📖

      
_{Manuel Calzolari}
💻

    

  

This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!