# 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-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-32-orange.svg?style=flat-square)](#contributors-)
<!-- 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>
[![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.
</div>
## 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
π [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
π [Anomaly Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals.
π©βπ¬ [Cross Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): robust modelβs performance evaluation.
βοΈ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL.
π [Predict Demand Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills.
π [Intermittent Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations.
π‘οΈ [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)|β
|β
|β
|β
|β
|
|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|β
|β
|β
|β
||
|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|β
|β
|β
|β
||
|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#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](https://nixtla.github.io/statsforecast/src/core/models.html#arima)|β
|β
|β
|β
|β
|
|[AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#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](https://nixtla.github.io/statsforecast/src/core/models.html#theta)|β
|β
|β
|β
||
|[OptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#optimizedtheta)|β
|β
|β
|β
||
|[DynamicTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamictheta)|β
|β
|β
|β
||
|[DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#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](https://nixtla.github.io/statsforecast/src/core/models.html#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](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|β
|β
|β
|β
||
|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#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](https://nixtla.github.io/statsforecast/src/core/models.html#historicaverage)|β
|β
|β
|β
||
|[Naive](https://nixtla.github.io/statsforecast/src/core/models.html#naive)|β
|β
|β
|β
||
|[RandomWalkWithDrift](https://nixtla.github.io/statsforecast/src/core/models.html#randomwalkwithdrift)|β
|β
|β
|β
||
|[SeasonalNaive](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalnaive)|β
|β
|β
|β
||
|[WindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#windowaverage)|β
|||||
|[SeasonalWindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#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](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)|β
|||||
|[SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)|β
|||||
|[SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)|β
|||||
|[SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized)|β
|||||
|[Holt](https://nixtla.github.io/statsforecast/src/core/models.html#holt)|β
|β
|β
|β
||
|[HoltWinters](https://nixtla.github.io/statsforecast/src/core/models.html#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](https://nixtla.github.io/statsforecast/src/core/models.html#adida)|β
||β
|β
||
|[CrostonClassic](https://nixtla.github.io/statsforecast/src/core/models.html#crostonclassic)|β
||β
|β
||
|[CrostonOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#crostonoptimized)|β
||β
|β
||
|[CrostonSBA](https://nixtla.github.io/statsforecast/src/core/models.html#crostonsba)|β
||β
|β
||
|[IMAPA](https://nixtla.github.io/statsforecast/src/core/models.html#imapa)|β
||β
|β
||
|[TSB](https://nixtla.github.io/statsforecast/src/core/models.html#tsb)|β
||β
|β
||
## π¨ How to contribute
See [CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md).
## Citing
```bibtex
@misc{garza2022statsforecast,
author={Federico 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](https://allcontributors.org/docs/en/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/FedericoGarza"><img src="https://avatars.githubusercontent.com/u/10517170?v=4?s=100" width="100px;" alt="fede"/><br /><sub><b>fede</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=FedericoGarza" title="Code">π»</a> <a href="#maintenance-FedericoGarza" 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](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
Raw data
{
"_id": null,
"home_page": "https://github.com/Nixtla/statsforecast/",
"name": "statsforecast",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "time-series forecasting arima ets",
"author": "Nixtla",
"author_email": "business@nixtla.io",
"download_url": "https://files.pythonhosted.org/packages/38/ef/4f5157b9c1749ec13d12d721bc8af0dda90c5c4f0c75948db31d93b71829/statsforecast-1.7.8.tar.gz",
"platform": null,
"description": "# 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)\n<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->\n[![All Contributors](https://img.shields.io/badge/all_contributors-32-orange.svg?style=flat-square)](#contributors-)\n<!-- ALL-CONTRIBUTORS-BADGE:END -->\n\n<div align=\"center\">\n<img src=\"https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png\">\n<h1 align=\"center\">Statistical \u26a1\ufe0f Forecast</h1>\n<h3 align=\"center\">Lightning fast forecasting with statistical and econometric models</h3>\n \n[![CI](https://github.com/Nixtla/statsforecast/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/Nixtla/statsforecast/actions/workflows/ci.yaml)\n[![Python](https://img.shields.io/pypi/pyversions/statsforecast)](https://pypi.org/project/statsforecast/)\n[![PyPi](https://img.shields.io/pypi/v/statsforecast?color=blue)](https://pypi.org/project/statsforecast/)\n[![conda-nixtla](https://img.shields.io/conda/vn/conda-forge/statsforecast?color=seagreen&label=conda)](https://anaconda.org/conda-forge/statsforecast)\n[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/Nixtla/statsforecast/blob/main/LICENSE)\n[![docs](https://img.shields.io/website-up-down-green-red/http/nixtla.github.io/statsforecast.svg?label=docs)](https://nixtla.github.io/statsforecast/)\n[![Downloads](https://pepy.tech/badge/statsforecast)](https://pepy.tech/project/statsforecast)\n \n**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.\n</div>\n\n## Installation\n\nYou can install `StatsForecast` with:\n\n```python\npip install statsforecast\n```\n\nor \n\n```python\nconda install -c conda-forge statsforecast\n``` \n\n\nVist our [Installation Guide](https://nixtla.github.io/statsforecast/docs/getting-started/installation.html) for further instructions.\n\n## Quick Start\n\n**Minimal Example**\n\n```python\nfrom statsforecast import StatsForecast\nfrom statsforecast.models import AutoARIMA\nfrom statsforecast.utils import AirPassengersDF\n\ndf = AirPassengersDF\nsf = StatsForecast(\n models = [AutoARIMA(season_length = 12)],\n freq = 'M'\n)\n\nsf.fit(df)\nsf.predict(h=12, level=[95])\n```\n\n**Get Started with this [quick guide](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_short.html).**\n\n**Follow this [end-to-end walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html) for best practices.**\n\n## Why? \n\nCurrent 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.\n\n## Features\n\n* Fastest and most accurate implementations of `AutoARIMA`, `AutoETS`, `AutoCES`, `MSTL` and `Theta` in Python. \n* Out-of-the-box compatibility with Spark, Dask, and Ray.\n* Probabilistic Forecasting and Confidence Intervals.\n* Support for exogenous Variables and static covariates.\n* Anomaly Detection.\n* Familiar sklearn syntax: `.fit` and `.predict`.\n\n## Highlights\n\n* Inclusion of `exogenous variables` and `prediction intervals` for ARIMA.\n* 20x [faster](./experiments/arima/) than `pmdarima`.\n* 1.5x faster than `R`.\n* 500x faster than `Prophet`. \n* 4x [faster](./experiments/ets/) than `statsmodels`.\n* Compiled to high performance machine code through [`numba`](https://numba.pydata.org/).\n* 1,000,000 series in [30 min](https://github.com/Nixtla/statsforecast/tree/main/experiments/ray) with [ray](https://github.com/ray-project/ray).\n* 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).\n* Fit 10 benchmark models on **1,000,000** series in [under **5 min**](./experiments/benchmarks_at_scale/). \n\n\nMissing 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)\n\n## Examples and Guides\n\n\ud83d\udcda [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\n\n\ud83d\udd0e [Anomaly Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals.\n\n\ud83d\udc69\u200d\ud83d\udd2c [Cross Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): robust model\u2019s performance evaluation.\n\n\u2744\ufe0f [Multiple Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL.\n\n\ud83d\udd0c [Predict Demand Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills.\n\n\ud83d\udcc8 [Intermittent Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations. \n\n\ud83c\udf21\ufe0f [Exogenous Regressors](https://nixtla.github.io/statsforecast/docs/how-to-guides/exogenous.html): like weather or prices\n\n\n## Models\n\n### Automatic Forecasting\nAutomatic 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.\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[AutoARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#autoarima)|\u2705|\u2705|\u2705|\u2705|\u2705|\n|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|\u2705|\u2705|\u2705|\u2705||\n|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|\u2705|\u2705|\u2705|\u2705||\n|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta)|\u2705|\u2705|\u2705|\u2705||\n\n### ARIMA Family\nThese models exploit the existing autocorrelations in the time series.\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[ARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#arima)|\u2705|\u2705|\u2705|\u2705|\u2705|\n|[AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#autoregressive)|\u2705|\u2705|\u2705|\u2705|\u2705|\n\n### Theta Family\nFit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts.\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[Theta](https://nixtla.github.io/statsforecast/src/core/models.html#theta)|\u2705|\u2705|\u2705|\u2705||\n|[OptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#optimizedtheta)|\u2705|\u2705|\u2705|\u2705||\n|[DynamicTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamictheta)|\u2705|\u2705|\u2705|\u2705||\n|[DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamicoptimizedtheta)|\u2705|\u2705|\u2705|\u2705||\n\n### Multiple Seasonalities\nSuited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs.\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[MSTL](https://nixtla.github.io/statsforecast/src/core/models.html#mstl)|\u2705|\u2705|\u2705|\u2705|If trend forecaster supports|\n\n### GARCH and ARCH Models \nSuited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH. \n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[GARCH](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|\u2705|\u2705|\u2705|\u2705||\n|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch)|\u2705|\u2705|\u2705|\u2705||\n\n\n### Baseline Models\nClassical models for establishing baseline.\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[HistoricAverage](https://nixtla.github.io/statsforecast/src/core/models.html#historicaverage)|\u2705|\u2705|\u2705|\u2705||\n|[Naive](https://nixtla.github.io/statsforecast/src/core/models.html#naive)|\u2705|\u2705|\u2705|\u2705||\n|[RandomWalkWithDrift](https://nixtla.github.io/statsforecast/src/core/models.html#randomwalkwithdrift)|\u2705|\u2705|\u2705|\u2705||\n|[SeasonalNaive](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalnaive)|\u2705|\u2705|\u2705|\u2705||\n|[WindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#windowaverage)|\u2705|||||\n|[SeasonalWindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalwindowaverage)|\u2705|||||\n\n### Exponential Smoothing\nUses 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.\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[SimpleExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)|\u2705|||||\n|[SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)|\u2705|||||\n|[SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)|\u2705|||||\n|[SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized)|\u2705|||||\n|[Holt](https://nixtla.github.io/statsforecast/src/core/models.html#holt)|\u2705|\u2705|\u2705|\u2705||\n|[HoltWinters](https://nixtla.github.io/statsforecast/src/core/models.html#holtwinters)|\u2705|\u2705|\u2705|\u2705||\n\n\n### Sparse or Intermittent \nSuited for series with very few non-zero observations\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[ADIDA](https://nixtla.github.io/statsforecast/src/core/models.html#adida)|\u2705||\u2705|\u2705||\n|[CrostonClassic](https://nixtla.github.io/statsforecast/src/core/models.html#crostonclassic)|\u2705||\u2705|\u2705||\n|[CrostonOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#crostonoptimized)|\u2705||\u2705|\u2705||\n|[CrostonSBA](https://nixtla.github.io/statsforecast/src/core/models.html#crostonsba)|\u2705||\u2705|\u2705||\n|[IMAPA](https://nixtla.github.io/statsforecast/src/core/models.html#imapa)|\u2705||\u2705|\u2705||\n|[TSB](https://nixtla.github.io/statsforecast/src/core/models.html#tsb)|\u2705||\u2705|\u2705||\n\n## \ud83d\udd28 How to contribute\nSee [CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md).\n\n## Citing\n\n```bibtex\n@misc{garza2022statsforecast,\n author={Federico Garza, Max Mergenthaler Canseco, Cristian Chall\u00fa, Kin G. Olivares},\n title = {{StatsForecast}: Lightning fast forecasting with statistical and econometric models},\n year={2022},\n howpublished={{PyCon} Salt Lake City, Utah, US 2022},\n url={https://github.com/Nixtla/statsforecast}\n}\n```\n\n## Contributors \u2728\n\nThanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):\n\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n<table>\n <tbody>\n <tr>\n <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/FedericoGarza\"><img src=\"https://avatars.githubusercontent.com/u/10517170?v=4?s=100\" width=\"100px;\" alt=\"fede\"/><br /><sub><b>fede</b></sub></a><br /><a href=\"https://github.com/Nixtla/statsforecast/commits?author=FedericoGarza\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#maintenance-FedericoGarza\" title=\"Maintenance\">\ud83d\udea7</a></td>\n <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=\"Jos\u00e9 Morales\"/><br /><sub><b>Jos\u00e9 Morales</b></sub></a><br /><a href=\"https://github.com/Nixtla/statsforecast/commits?author=jmoralez\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#maintenance-jmoralez\" title=\"Maintenance\">\ud83d\udea7</a></td>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udc1b</a></td>\n <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\">\ud83e\udd14</a></td>\n <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\">\ud83d\udcac</a></td>\n <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\">\ud83d\udcac</a></td>\n </tr>\n <tr>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83e\udd14</a></td>\n <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\">\ud83e\udd14</a></td>\n <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 Gilli\u00dfen\"/><br /><sub><b>Philip Gilli\u00dfen</b></sub></a><br /><a href=\"https://github.com/Nixtla/statsforecast/commits?author=guerda\" title=\"Code\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udc1b</a> <a href=\"https://github.com/Nixtla/statsforecast/commits?author=shagn\" title=\"Documentation\">\ud83d\udcd6</a></td>\n <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\">\ud83d\udcbb</a></td>\n </tr>\n <tr>\n <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\">\ud83d\udc1b</a></td>\n <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\">\ud83d\udcd6</a></td>\n <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 Garc\u00eda \"/><br /><sub><b>Mariana Menchero Garc\u00eda </b></sub></a><br /><a href=\"https://github.com/Nixtla/statsforecast/commits?author=MMenchero\" title=\"Code\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udc1b</a></td>\n <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\">\ud83d\udc1b</a></td>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udcbb</a></td>\n </tr>\n <tr>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udcd6</a></td>\n <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 C\u00e1rdenas Bola\u00f1o\"/><br /><sub><b>Nelson C\u00e1rdenas Bola\u00f1o</b></sub></a><br /><a href=\"https://github.com/Nixtla/statsforecast/commits?author=nelsoncardenas\" title=\"Documentation\">\ud83d\udcd6</a></td>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udcbb</a></td>\n <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\">\ud83d\udcbb</a></td>\n </tr>\n <tr>\n <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\">\ud83d\udcd6</a></td>\n <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\">\ud83d\udcd6</a></td>\n <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\">\ud83d\udcd6</a></td>\n <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\">\ud83d\udcbb</a></td>\n </tr>\n </tbody>\n</table>\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n\nThis project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!\n",
"bugtrack_url": null,
"license": "Apache Software License 2.0",
"summary": "Time series forecasting suite using statistical models",
"version": "1.7.8",
"project_urls": {
"Homepage": "https://github.com/Nixtla/statsforecast/"
},
"split_keywords": [
"time-series",
"forecasting",
"arima",
"ets"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "240559d9bfe4586f006ac5d06d7a6e1760f6ca5f31208bace43b64eff55c647a",
"md5": "2ed3ec3f9382b71ab2b4d28dbbf9c366",
"sha256": "79e5683a43265edd55d71b31bc602b29e8e38e61d0c9ef4b9000056ab6b8534e"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp310-cp310-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "2ed3ec3f9382b71ab2b4d28dbbf9c366",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 288129,
"upload_time": "2024-09-19T21:39:13",
"upload_time_iso_8601": "2024-09-19T21:39:13.509991Z",
"url": "https://files.pythonhosted.org/packages/24/05/59d9bfe4586f006ac5d06d7a6e1760f6ca5f31208bace43b64eff55c647a/statsforecast-1.7.8-cp310-cp310-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "6cc5d1891f334d500647ab05e9e5dea0b89724097d859b031051c7822d75c6e3",
"md5": "b9aba6ade8f2c935f771a51b6dc2ade7",
"sha256": "a205baeac2db8afae70a67498c9ec89762525d88c741e323af5a539e4260cc61"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp310-cp310-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "b9aba6ade8f2c935f771a51b6dc2ade7",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 273895,
"upload_time": "2024-09-19T21:39:14",
"upload_time_iso_8601": "2024-09-19T21:39:14.991384Z",
"url": "https://files.pythonhosted.org/packages/6c/c5/d1891f334d500647ab05e9e5dea0b89724097d859b031051c7822d75c6e3/statsforecast-1.7.8-cp310-cp310-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a535e662883ae62829f921cd93f7007b955206c2c46006f51ecd07ffa108ee84",
"md5": "09ad4991e3efccc82d7e8100f38e87e4",
"sha256": "12f1bea7e1a23ca425476dfcf17dc56a44462ce0284f8c6f82c65b49c5c23b0b"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "09ad4991e3efccc82d7e8100f38e87e4",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 304767,
"upload_time": "2024-09-19T21:39:16",
"upload_time_iso_8601": "2024-09-19T21:39:16.622066Z",
"url": "https://files.pythonhosted.org/packages/a5/35/e662883ae62829f921cd93f7007b955206c2c46006f51ecd07ffa108ee84/statsforecast-1.7.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "87dda5fc68cc69a1c81e545d44ba74fd0c6a4e3a69db27ef7fb35e10d6de1ca0",
"md5": "760364745a5c6c467d02a19dc7535488",
"sha256": "892413f04f48c6036ebefb759cf676b0cf98ce6079c3f1a55795eba3a06d20b2"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "760364745a5c6c467d02a19dc7535488",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 314748,
"upload_time": "2024-09-19T21:39:18",
"upload_time_iso_8601": "2024-09-19T21:39:18.236802Z",
"url": "https://files.pythonhosted.org/packages/87/dd/a5fc68cc69a1c81e545d44ba74fd0c6a4e3a69db27ef7fb35e10d6de1ca0/statsforecast-1.7.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2f9c9bc8b708ddf7ba852448ea821cf6ba04f9f482462bd0b6f9bd0d4c00e5f8",
"md5": "665a671b70af21683eb5853b5cb368f3",
"sha256": "e020832403530e5be74f44738d792c043faecc2718e71cc6cf1372f361210f64"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp310-cp310-win_amd64.whl",
"has_sig": false,
"md5_digest": "665a671b70af21683eb5853b5cb368f3",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 253547,
"upload_time": "2024-09-19T21:39:19",
"upload_time_iso_8601": "2024-09-19T21:39:19.967927Z",
"url": "https://files.pythonhosted.org/packages/2f/9c/9bc8b708ddf7ba852448ea821cf6ba04f9f482462bd0b6f9bd0d4c00e5f8/statsforecast-1.7.8-cp310-cp310-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b39ef71462cc00622b653bd9d1865b08840140267389f6fe50adcf9c8a5935fe",
"md5": "41155bec787e7082088166fb370f6687",
"sha256": "737c99fff0e881fb1713e7f38e5d3b523a6451650564706eef91ce647d5a8d74"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp311-cp311-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "41155bec787e7082088166fb370f6687",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 289388,
"upload_time": "2024-09-19T21:39:21",
"upload_time_iso_8601": "2024-09-19T21:39:21.241166Z",
"url": "https://files.pythonhosted.org/packages/b3/9e/f71462cc00622b653bd9d1865b08840140267389f6fe50adcf9c8a5935fe/statsforecast-1.7.8-cp311-cp311-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "fb8198990339028ee547a1d5b155f411d42b29df5cb6bad80830accb665dad09",
"md5": "92eed6e6bb9e4554e3ede5f4415e9cdd",
"sha256": "725ebea30e9098c812dfbc85dc31a5b459d46d3b81e68212899894770f27648a"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp311-cp311-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "92eed6e6bb9e4554e3ede5f4415e9cdd",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 275071,
"upload_time": "2024-09-19T21:39:22",
"upload_time_iso_8601": "2024-09-19T21:39:22.622490Z",
"url": "https://files.pythonhosted.org/packages/fb/81/98990339028ee547a1d5b155f411d42b29df5cb6bad80830accb665dad09/statsforecast-1.7.8-cp311-cp311-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a80f62317fadd4b5d9e1fe0b88d352cbad2e254a5c9ebfbba6d379d2096a8110",
"md5": "cb8d30462cd98bac75070aa85dc39ca3",
"sha256": "aa02344b08e71497ddc562b023ab627b3703f035fd703dad3e5a0f6e94cb8c7f"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "cb8d30462cd98bac75070aa85dc39ca3",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 305897,
"upload_time": "2024-09-19T21:39:24",
"upload_time_iso_8601": "2024-09-19T21:39:24.222977Z",
"url": "https://files.pythonhosted.org/packages/a8/0f/62317fadd4b5d9e1fe0b88d352cbad2e254a5c9ebfbba6d379d2096a8110/statsforecast-1.7.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8b0afd4477e16c0d9509d503891dbba2baf73f4b17426850c3c02350a18ab049",
"md5": "2888851d063ab7d722a65261cde1eca1",
"sha256": "84bc52363d66ece211e6d196eb764fceb097384a192031be866bea02f7e62c4d"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "2888851d063ab7d722a65261cde1eca1",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 315839,
"upload_time": "2024-09-19T21:39:25",
"upload_time_iso_8601": "2024-09-19T21:39:25.642208Z",
"url": "https://files.pythonhosted.org/packages/8b/0a/fd4477e16c0d9509d503891dbba2baf73f4b17426850c3c02350a18ab049/statsforecast-1.7.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "de7c4e56c0c5aa11f73cebd5e49ec55cc7be6799c559a290d2eac893cef18394",
"md5": "d6c9f1445519e2b2a2420ab1f7da3fbb",
"sha256": "da74148ab2e398ad4310c12fa34517477ad20fc49b935f63c330da2e94380c9f"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp311-cp311-win_amd64.whl",
"has_sig": false,
"md5_digest": "d6c9f1445519e2b2a2420ab1f7da3fbb",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 254717,
"upload_time": "2024-09-19T21:39:26",
"upload_time_iso_8601": "2024-09-19T21:39:26.988854Z",
"url": "https://files.pythonhosted.org/packages/de/7c/4e56c0c5aa11f73cebd5e49ec55cc7be6799c559a290d2eac893cef18394/statsforecast-1.7.8-cp311-cp311-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0eff908343552251642c8d2dd374e5b909d2c2d16ba6abc60f9d2a24076c65e9",
"md5": "3d0ba26f34f1ee03c39ea0d80dd353ca",
"sha256": "33eace24080e559ac410fbed361cabfe688cb2f9b9a2e2f20aca19dcbc20be0d"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp312-cp312-macosx_10_13_x86_64.whl",
"has_sig": false,
"md5_digest": "3d0ba26f34f1ee03c39ea0d80dd353ca",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 289014,
"upload_time": "2024-09-19T21:39:28",
"upload_time_iso_8601": "2024-09-19T21:39:28.456396Z",
"url": "https://files.pythonhosted.org/packages/0e/ff/908343552251642c8d2dd374e5b909d2c2d16ba6abc60f9d2a24076c65e9/statsforecast-1.7.8-cp312-cp312-macosx_10_13_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a5f5e18a4a5d1023159411c0080095b1ee673e6f78493f1ad5c32b2e17ebe1d8",
"md5": "6d65535bc51f0e7f226b54946deb2781",
"sha256": "a598bedc662ae892d5ab050352746805a731a8aec511a6771cf03b1f4d76e3f2"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp312-cp312-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "6d65535bc51f0e7f226b54946deb2781",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 274234,
"upload_time": "2024-09-19T21:39:29",
"upload_time_iso_8601": "2024-09-19T21:39:29.939307Z",
"url": "https://files.pythonhosted.org/packages/a5/f5/e18a4a5d1023159411c0080095b1ee673e6f78493f1ad5c32b2e17ebe1d8/statsforecast-1.7.8-cp312-cp312-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "44d0f96049db657cb1f17f6513dc2bcd4d647ce10dcf2225260b23d1b3210ac7",
"md5": "6dd2b4d8412a4a67fd58377321a9b0ea",
"sha256": "6eda1b19e9230925766d2f247ab64d873af09ba0db56d8ddf2705aa78e4d59b1"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "6dd2b4d8412a4a67fd58377321a9b0ea",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 305214,
"upload_time": "2024-09-19T21:39:31",
"upload_time_iso_8601": "2024-09-19T21:39:31.153605Z",
"url": "https://files.pythonhosted.org/packages/44/d0/f96049db657cb1f17f6513dc2bcd4d647ce10dcf2225260b23d1b3210ac7/statsforecast-1.7.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "6b6fe70e0f3937ecbe65f353ba3df5385f6996e240fb366d3a2e1cfd7cc2daf4",
"md5": "c37f91e56f2ef42b24e37b03e8239161",
"sha256": "c949af0b8b299bd1cf541de9b718c9f4ad627cd858fbda0a14bc4fd5d390241e"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "c37f91e56f2ef42b24e37b03e8239161",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 315181,
"upload_time": "2024-09-19T21:39:32",
"upload_time_iso_8601": "2024-09-19T21:39:32.754680Z",
"url": "https://files.pythonhosted.org/packages/6b/6f/e70e0f3937ecbe65f353ba3df5385f6996e240fb366d3a2e1cfd7cc2daf4/statsforecast-1.7.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e6ab5de37ea549895b9ee5701ac45bb7aff47ec7838754ab468188af7253b699",
"md5": "13c2d93138e74aad67301bd5b83ae5d2",
"sha256": "1815e702096e5da75add79c81b3cc867954f3a957c921836035a78065f8e720e"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp312-cp312-win_amd64.whl",
"has_sig": false,
"md5_digest": "13c2d93138e74aad67301bd5b83ae5d2",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 255437,
"upload_time": "2024-09-19T21:39:34",
"upload_time_iso_8601": "2024-09-19T21:39:34.479058Z",
"url": "https://files.pythonhosted.org/packages/e6/ab/5de37ea549895b9ee5701ac45bb7aff47ec7838754ab468188af7253b699/statsforecast-1.7.8-cp312-cp312-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c8b6bdcc02a7949dd5389065433dcc28cb5f99ed1401ba57e95eb52e9085ce95",
"md5": "604dc9b2944b1a6a3c954ca53e6cf2c9",
"sha256": "656f9bbd328557aec776c8ce90f1a0c7601b4ca391454eae348c24ae88f7a1d5"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp38-cp38-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "604dc9b2944b1a6a3c954ca53e6cf2c9",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 288025,
"upload_time": "2024-09-19T21:39:36",
"upload_time_iso_8601": "2024-09-19T21:39:36.048428Z",
"url": "https://files.pythonhosted.org/packages/c8/b6/bdcc02a7949dd5389065433dcc28cb5f99ed1401ba57e95eb52e9085ce95/statsforecast-1.7.8-cp38-cp38-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0d555baad59eeda756bd17d972dfc55b8716821355977df7a96991fb9e1f0838",
"md5": "3830555ea949c7916314eab7f385c3eb",
"sha256": "785c08a87bacbb4613b8e5d8273f6b31e2e8f3752f651bd695219d43656b6738"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp38-cp38-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "3830555ea949c7916314eab7f385c3eb",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 273735,
"upload_time": "2024-09-19T21:39:37",
"upload_time_iso_8601": "2024-09-19T21:39:37.194845Z",
"url": "https://files.pythonhosted.org/packages/0d/55/5baad59eeda756bd17d972dfc55b8716821355977df7a96991fb9e1f0838/statsforecast-1.7.8-cp38-cp38-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "3eaf570719df38f8a26a1b992ddb67710cf155d35ea4c10fa6a8fa63e436ab09",
"md5": "1eeabfd816b3c2afd8bccd506d3b708f",
"sha256": "6fa9babfd8fb046912d2dde747f79f1957c20b717fc65a9f91c9994d8db6a737"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "1eeabfd816b3c2afd8bccd506d3b708f",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 304954,
"upload_time": "2024-09-19T21:39:38",
"upload_time_iso_8601": "2024-09-19T21:39:38.261966Z",
"url": "https://files.pythonhosted.org/packages/3e/af/570719df38f8a26a1b992ddb67710cf155d35ea4c10fa6a8fa63e436ab09/statsforecast-1.7.8-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c7e1097d1ef07733808ee5f73843334ab977dbbf9f231c91be2f5e2d94ec0851",
"md5": "9562e607ae603d16d6a13580b841a907",
"sha256": "d24b2a1f8e88bbeda3905bae7adc9ccffce40bbf10b64bd1121600fed958b9fa"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "9562e607ae603d16d6a13580b841a907",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 313631,
"upload_time": "2024-09-19T21:39:40",
"upload_time_iso_8601": "2024-09-19T21:39:40.099945Z",
"url": "https://files.pythonhosted.org/packages/c7/e1/097d1ef07733808ee5f73843334ab977dbbf9f231c91be2f5e2d94ec0851/statsforecast-1.7.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "fe4a351eff0c4b3b3ada7e5d00d0a64937d3151fb7a8c60052a17c0c951dd58a",
"md5": "a6bc5f24845ada5141019480e187874a",
"sha256": "be8383b621bd5805ae003bfacded6c9eb3bb156d6948c27077cedd58314b862f"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp38-cp38-win_amd64.whl",
"has_sig": false,
"md5_digest": "a6bc5f24845ada5141019480e187874a",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 253618,
"upload_time": "2024-09-19T21:39:41",
"upload_time_iso_8601": "2024-09-19T21:39:41.735889Z",
"url": "https://files.pythonhosted.org/packages/fe/4a/351eff0c4b3b3ada7e5d00d0a64937d3151fb7a8c60052a17c0c951dd58a/statsforecast-1.7.8-cp38-cp38-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "9bb6e347ad8203a6157d2846ceb0b246d46b58d7c14b23030f32e899b0bef4e3",
"md5": "825d93273da12e87090d31a38e6725b2",
"sha256": "34351e94006ad83fac45a78667731923b131bed5149b9f96c6bf249117b7612e"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp39-cp39-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "825d93273da12e87090d31a38e6725b2",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 288301,
"upload_time": "2024-09-19T21:39:43",
"upload_time_iso_8601": "2024-09-19T21:39:43.403577Z",
"url": "https://files.pythonhosted.org/packages/9b/b6/e347ad8203a6157d2846ceb0b246d46b58d7c14b23030f32e899b0bef4e3/statsforecast-1.7.8-cp39-cp39-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "f13b8a2f0ec9ff5295e5541ed7ebb0587a3aa16c6310b5ea4b093354cda2fbfd",
"md5": "7f5662352c4ab68e37c68c20118816ab",
"sha256": "b51895e1dd88ab8abe0c43135822e4f5eafd0a903387407c21eda9e610cf00af"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp39-cp39-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "7f5662352c4ab68e37c68c20118816ab",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 273998,
"upload_time": "2024-09-19T21:39:44",
"upload_time_iso_8601": "2024-09-19T21:39:44.550676Z",
"url": "https://files.pythonhosted.org/packages/f1/3b/8a2f0ec9ff5295e5541ed7ebb0587a3aa16c6310b5ea4b093354cda2fbfd/statsforecast-1.7.8-cp39-cp39-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "3d06061e189b0d0d82f257f635f6a3f00a39dea962dd13a58e2674fae6275d20",
"md5": "1c9785455ef6dd2dc12ad68a64edc6b8",
"sha256": "77f4d9d32c5c4df65720a71721af405f4d348f341ade89d1cabbdc7f02af73a9"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"has_sig": false,
"md5_digest": "1c9785455ef6dd2dc12ad68a64edc6b8",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 305035,
"upload_time": "2024-09-19T21:39:45",
"upload_time_iso_8601": "2024-09-19T21:39:45.651490Z",
"url": "https://files.pythonhosted.org/packages/3d/06/061e189b0d0d82f257f635f6a3f00a39dea962dd13a58e2674fae6275d20/statsforecast-1.7.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "eebf95e92580072ed8cc0998beac933425c9dc78faf0f0998686cf8303e3e24d",
"md5": "4e936289cd51a07f1613c1983aac7ede",
"sha256": "90df1972fcc6fe945a0e80398174f9a88fefc3ef1980e24a045e7029e9cf1eb2"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "4e936289cd51a07f1613c1983aac7ede",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 314859,
"upload_time": "2024-09-19T21:39:47",
"upload_time_iso_8601": "2024-09-19T21:39:47.627222Z",
"url": "https://files.pythonhosted.org/packages/ee/bf/95e92580072ed8cc0998beac933425c9dc78faf0f0998686cf8303e3e24d/statsforecast-1.7.8-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "10dd8f16affb59ff29693f94e106e414e42f029bef48ca5aa842f4d0822f0d51",
"md5": "08aa74d1d1385adfc34c9888392fa3b1",
"sha256": "1a4f6364e3542109a46efc4c67f28a2172e0b3805b4001595207a0f8fa08c715"
},
"downloads": -1,
"filename": "statsforecast-1.7.8-cp39-cp39-win_amd64.whl",
"has_sig": false,
"md5_digest": "08aa74d1d1385adfc34c9888392fa3b1",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 251840,
"upload_time": "2024-09-19T21:39:48",
"upload_time_iso_8601": "2024-09-19T21:39:48.961764Z",
"url": "https://files.pythonhosted.org/packages/10/dd/8f16affb59ff29693f94e106e414e42f029bef48ca5aa842f4d0822f0d51/statsforecast-1.7.8-cp39-cp39-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "38ef4f5157b9c1749ec13d12d721bc8af0dda90c5c4f0c75948db31d93b71829",
"md5": "6daef3791c06d330a7f3690805968477",
"sha256": "44a89372e1761bba90ded17ccd6fd3504012d219e8ceb4c453d78be910e92287"
},
"downloads": -1,
"filename": "statsforecast-1.7.8.tar.gz",
"has_sig": false,
"md5_digest": "6daef3791c06d330a7f3690805968477",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 2880799,
"upload_time": "2024-09-19T21:39:50",
"upload_time_iso_8601": "2024-09-19T21:39:50.147477Z",
"url": "https://files.pythonhosted.org/packages/38/ef/4f5157b9c1749ec13d12d721bc8af0dda90c5c4f0c75948db31d93b71829/statsforecast-1.7.8.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-19 21:39:50",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Nixtla",
"github_project": "statsforecast",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "statsforecast"
}