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Nixtla\n\n[![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\u0026url=https://github.com/Nixtla/statsforecast\u0026via=nixtlainc\u0026hashtags=StatisticalModels,TimeSeries,Forecasting)\n[![Slack](https://img.shields.io/badge/Slack-4A154B?\u0026logo=slack\u0026logoColor=white)](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ)\n\n[![All Contributors](https://img.shields.io/badge/all_contributors-32-orange.svg?style=flat-square)](#contributors-)\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png\"/\u003e\n\u003ch1 align=\"center\"\u003eStatistical ⚡️ Forecast\u003c/h1\u003e\n\u003ch3 align=\"center\"\u003eLightning fast forecasting with statistical and econometric models\u003c/h3\u003e\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\u0026label=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://github.com/Nixtla/statsforecast/actions/workflows/build-docs.yaml/badge.svg)](https://github.com/Nixtla/statsforecast/actions/workflows/build-docs.yaml)\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. It also includes a large battery of benchmarking models.\n\u003c/div\u003e\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\nVist our [Installation Guide](https://nixtlaverse.nixtla.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='ME',\n)\nsf.fit(df)\nsf.predict(h=12, level=[95])\n```\n\n**Get Started [quick guide](https://nixtlaverse.nixtla.io/statsforecast/docs/getting-started/getting_started_short.html)**\n\n**Follow this [end-to-end walkthrough](https://nixtlaverse.nixtla.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](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima) than `pmdarima`.\n* 1.5x faster than `R`.\n* 500x faster than `Prophet`.\n* 4x [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/ets) than `statsmodels`.\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**](https://github.com/Nixtla/statsforecast/tree/main/experiments/benchmarks_at_scale/).\n\nMissing something? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?\u0026logo=slack\u0026logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A)\n\n## Examples and Guides\n\n📚 [End to End Walkthrough](https://nixtlaverse.nixtla.io/statsforecast/docs/getting-started/getting_started_complete.html): Model training, evaluation and selection for multiple time series\n\n🔎 [Anomaly Detection](https://nixtlaverse.nixtla.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals.\n\n👩‍🔬 [Cross Validation](https://nixtlaverse.nixtla.io/statsforecast/docs/tutorials/crossvalidation.html): robust model’s performance evaluation.\n\n❄️ [Multiple Seasonalities](https://nixtlaverse.nixtla.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL.\n\n🔌 [Predict Demand Peaks](https://nixtlaverse.nixtla.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills.\n\n📈 [Intermittent Demand](https://nixtlaverse.nixtla.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations.\n\n🌡️ [Exogenous Regressors](https://nixtlaverse.nixtla.io/statsforecast/docs/how-to-guides/exogenous.html): like weather or prices\n\n## Models\n\n### Automatic Forecasting\n\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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoarima)|✅|✅|✅|✅|✅|\n|[AutoETS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoets)|✅|✅|✅|✅||\n|[AutoCES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoces)|✅|✅|✅|✅||\n|[AutoTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotheta)|✅|✅|✅|✅||\n|[AutoMFLES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#automfles)|✅|✅|✅|✅|✅|\n|[AutoTBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats)|✅|✅|✅|✅||\n\n### ARIMA Family\n\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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#arima)|✅|✅|✅|✅|✅|\n|[AutoRegressive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoregressive)|✅|✅|✅|✅|✅|\n\n### Theta Family\n\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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#theta)|✅|✅|✅|✅|✅|\n|[OptimizedTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#optimizedtheta)|✅|✅|✅|✅||\n|[DynamicTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#dynamictheta)|✅|✅|✅|✅||\n|[DynamicOptimizedTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#dynamicoptimizedtheta)|✅|✅|✅|✅||\n\n### Multiple Seasonalities\n\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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#mstl)|✅|✅|✅|✅|If trend forecaster supports|\n|[MFLES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#mfles)|✅|✅|✅|✅|✅|\n|[TBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tbats)|✅|✅|✅|✅||\n\n### GARCH and ARCH Models\n\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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#garch)|✅|✅|✅|✅||\n|[ARCH](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#arch)|✅|✅|✅|✅||\n\n### Baseline Models\n\nClassical models for establishing baseline.\n\n|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|\n|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|\n|[HistoricAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#historicaverage)|✅|✅|✅|✅||\n|[Naive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#naive)|✅|✅|✅|✅||\n|[RandomWalkWithDrift](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#randomwalkwithdrift)|✅|✅|✅|✅||\n|[SeasonalNaive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalnaive)|✅|✅|✅|✅||\n|[WindowAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#windowaverage)|✅|||||\n|[SeasonalWindowAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalwindowaverage)|✅|||||\n\n### Exponential Smoothing\n\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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)|✅||✅|||\n|[SimpleExponentialSmoothingOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)|✅||✅|||\n|[SeasonalExponentialSmoothing](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)|✅||✅|||\n|[SeasonalExponentialSmoothingOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized)|✅||✅|||\n|[Holt](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#holt)|✅|✅|✅|✅||\n|[HoltWinters](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#holtwinters)|✅|✅|✅|✅||\n\n### Sparse or Inttermitent\n\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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#adida)|✅||✅|✅||\n|[CrostonClassic](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonclassic)|✅||✅|✅||\n|[CrostonOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonoptimized)|✅||✅|✅||\n|[CrostonSBA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonsba)|✅||✅|✅||\n|[IMAPA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#imapa)|✅||✅|✅||\n|[TSB](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tsb)|✅||✅|✅||\n\n## 🔨 How to contribute\n\nSee [CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md).\n\n## Citing\n\n```bibtex\n@misc{garza2022statsforecast,\n    author={Azul Garza, Max Mergenthaler Canseco, Cristian Challú, 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 ✨\n\nThanks goes to these wonderful people ([emoji key](https://allcontributors.org/en/reference/emoji-key/)):\n\n\u003ctable\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/AzulGarza\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/10517170?v=4?s=100\" width=\"100px;\" alt=\"azul\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eazul\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=AzulGarza\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"#maintenance-AzulGarza\" title=\"Maintenance\"\u003e🚧\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/jmoralez\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/8473587?v=4?s=100\" width=\"100px;\" alt=\"José Morales\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJosé Morales\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=jmoralez\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"#maintenance-jmoralez\" title=\"Maintenance\"\u003e🚧\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://www.linkedin.com/in/sugatoray/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/10201242?v=4?s=100\" width=\"100px;\" alt=\"Sugato Ray\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eSugato Ray\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=sugatoray\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/tackes\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/9125316?v=4?s=100\" width=\"100px;\" alt=\"Jeff Tackes\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJeff Tackes\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/issues?q=author%3Atackes\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/darinkist\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/62692170?v=4?s=100\" width=\"100px;\" alt=\"darinkist\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003edarinkist\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-darinkist\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/alech97\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/22159405?v=4?s=100\" width=\"100px;\" alt=\"Alec Helyar\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eAlec Helyar\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#question-alech97\" title=\"Answering Questions\"\u003e💬\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://dhirschfeld.github.io\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/881019?v=4?s=100\" width=\"100px;\" alt=\"Dave Hirschfeld\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eDave Hirschfeld\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#question-dhirschfeld\" title=\"Answering Questions\"\u003e💬\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/mergenthaler\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/4086186?v=4?s=100\" width=\"100px;\" alt=\"mergenthaler\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003emergenthaler\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=mergenthaler\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/kdgutier\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/19935241?v=4?s=100\" width=\"100px;\" alt=\"Kin\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eKin\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=kdgutier\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/Yasslight90\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/58293883?v=4?s=100\" width=\"100px;\" alt=\"Yasslight90\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eYasslight90\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-Yasslight90\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/asinig\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/99350687?v=4?s=100\" width=\"100px;\" alt=\"asinig\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003easinig\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-asinig\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/guerda\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/230782?v=4?s=100\" width=\"100px;\" alt=\"Philip Gillißen\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003ePhilip Gillißen\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=guerda\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/shagn\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/16029092?v=4?s=100\" width=\"100px;\" alt=\"Sebastian Hagn\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eSebastian Hagn\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/issues?q=author%3Ashagn\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e \u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=shagn\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/fugue-project/fugue\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/21092479?v=4?s=100\" width=\"100px;\" alt=\"Han Wang\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eHan Wang\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=goodwanghan\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://www.linkedin.com/in/benjamin-jeffrey-218548a8/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/36240394?v=4?s=100\" width=\"100px;\" alt=\"Ben Jeffrey\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eBen Jeffrey\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/issues?q=author%3Abjeffrey92\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/Beliavsky\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/38887928?v=4?s=100\" width=\"100px;\" alt=\"Beliavsky\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eBeliavsky\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=Beliavsky\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/MMenchero\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/47995617?v=4?s=100\" width=\"100px;\" alt=\"Mariana Menchero García \"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eMariana Menchero García \u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=MMenchero\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://www.linkedin.com/in/guptanick/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/33585645?v=4?s=100\" width=\"100px;\" alt=\"Nikhil Gupta\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eNikhil Gupta\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/issues?q=author%3Angupta23\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/jdegene\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/17744939?v=4?s=100\" width=\"100px;\" alt=\"JD\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJD\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/issues?q=author%3Ajdegene\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/jattenberg\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/924185?v=4?s=100\" width=\"100px;\" alt=\"josh attenberg\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003ejosh attenberg\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=jattenberg\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/JeroenPeterBos\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/15342738?v=4?s=100\" width=\"100px;\" alt=\"JeroenPeterBos\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJeroenPeterBos\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=JeroenPeterBos\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/jvdd\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/18898740?v=4?s=100\" width=\"100px;\" alt=\"Jeroen Van Der Donckt\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJeroen Van Der Donckt\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=jvdd\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/Roymprog\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/4035367?v=4?s=100\" width=\"100px;\" alt=\"Roymprog\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eRoymprog\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=Roymprog\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/nelsoncardenas\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/18086414?v=4?s=100\" width=\"100px;\" alt=\"Nelson Cárdenas Bolaño\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eNelson Cárdenas Bolaño\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=nelsoncardenas\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/kschmaus\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/6586847?v=4?s=100\" width=\"100px;\" alt=\"Kyle Schmaus\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eKyle Schmaus\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=kschmaus\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://www.linkedin.com/in/akmal-soliev/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/24494206?v=4?s=100\" width=\"100px;\" alt=\"Akmal Soliev\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eAkmal Soliev\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=akmalsoliev\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/nickto\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/11967792?v=4?s=100\" width=\"100px;\" alt=\"Nick To\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eNick To\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=nickto\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://www.linkedin.com/in/kvnkho/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/32503212?v=4?s=100\" width=\"100px;\" alt=\"Kevin Kho\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eKevin Kho\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=kvnkho\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/yibenhuang\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/62163340?v=4?s=100\" width=\"100px;\" alt=\"Yiben Huang\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eYiben Huang\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=yibenhuang\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/andrewgross\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/370118?v=4?s=100\" width=\"100px;\" alt=\"Andrew Gross\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eAndrew Gross\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=andrewgross\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/taniishkaaa\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/109246904?v=4?s=100\" width=\"100px;\" alt=\"taniishkaaa\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003etaniishkaaa\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=taniishkaaa\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/manuel-calzolari\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/2764902?v=4?s=100\" width=\"100px;\" alt=\"Manuel Calzolari\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eManuel Calzolari\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast/commits?author=manuel-calzolari\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\nThis project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnixtla%2Fstatsforecast","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnixtla%2Fstatsforecast","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnixtla%2Fstatsforecast/lists"}