> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

> Lightning fast forecasting with statistical and econometric models

# Statistical ⚡️ Forecast

## Installation

You can install `StatsForecast` with:

```python theme={null}
pip install statsforecast
```

or

```python theme={null}
conda install -c conda-forge statsforecast
```

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

## Quick Start

**Minimal Example**

```python theme={null}
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
from statsforecast.utils import AirPassengersDF

df = AirPassengersDF
sf = StatsForecast(
    models=[AutoARIMA(season_length=12)],
    freq='ME',
)
sf.fit(df)
sf.predict(h=12, level=[95])
```

**Get Started [quick guide](https://nixtlaverse.nixtla.io/statsforecast/docs/getting-started/getting_started_short.html)**

**Follow this [end-to-end walkthrough](https://nixtlaverse.nixtla.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](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima) than `pmdarima`.
* 1.5x faster than `R`.
* 500x faster than `Prophet`.
* 4x [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/ets) than `statsmodels`.
* 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**](https://github.com/Nixtla/statsforecast/tree/main/experiments/benchmarks_at_scale/).

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

## Examples and Guides

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

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

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

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

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

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

🌡️ [Exogenous Regressors](https://nixtlaverse.nixtla.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://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoarima) |        ✅       |            ✅           |            ✅           |              ✅              |          ✅         |
| [AutoETS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoets)     |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [AutoCES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoces)     |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [AutoTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotheta) |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [AutoMFLES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#automfles) |        ✅       |            ✅           |            ✅           |              ✅              |          ✅         |
| [AutoTBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats) |        ✅       |            ✅           |            ✅           |              ✅              |                    |

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

### Theta Family

Fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts.

| Model                                                                                                           | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
| :-------------------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: |
| [Theta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#theta)                                 |        ✅       |            ✅           |            ✅           |              ✅              |          ✅         |
| [OptimizedTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#optimizedtheta)               |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [DynamicTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#dynamictheta)                   |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [DynamicOptimizedTheta](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#dynamicoptimizedtheta) |        ✅       |            ✅           |            ✅           |              ✅              |                    |

### Multiple Seasonalities

Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs.

| Model                                                                           | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |      Exogenous features      |
| :------------------------------------------------------------------------------ | :------------: | :--------------------: | :--------------------: | :-------------------------: | :--------------------------: |
| [MSTL](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#mstl)   |        ✅       |            ✅           |            ✅           |              ✅              | If trend forecaster supports |
| [MFLES](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#mfles) |        ✅       |            ✅           |            ✅           |              ✅              |               ✅              |
| [TBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tbats) |        ✅       |            ✅           |            ✅           |              ✅              |                              |

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

### Baseline Models

Classical models for establishing baseline.

| Model                                                                                                           | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
| :-------------------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: |
| [HistoricAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#historicaverage)             |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [Naive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#naive)                                 |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [RandomWalkWithDrift](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#randomwalkwithdrift)     |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [SeasonalNaive](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalnaive)                 |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [WindowAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#windowaverage)                 |        ✅       |                        |                        |                             |                    |
| [SeasonalWindowAverage](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalwindowaverage) |        ✅       |                        |                        |                             |                    |

### Exponential Smoothing

Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with clear trend and/or seasonality. Use the `SimpleExponential` family for data with no clear trend or seasonality.

| Model                                                                                                                                           | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
| :---------------------------------------------------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: |
| [SimpleExponentialSmoothing](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)                       |        ✅       |                        |            ✅           |                             |                    |
| [SimpleExponentialSmoothingOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)     |        ✅       |                        |            ✅           |                             |                    |
| [SeasonalExponentialSmoothing](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)                   |        ✅       |                        |            ✅           |                             |                    |
| [SeasonalExponentialSmoothingOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized) |        ✅       |                        |            ✅           |                             |                    |
| [Holt](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#holt)                                                                   |        ✅       |            ✅           |            ✅           |              ✅              |                    |
| [HoltWinters](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#holtwinters)                                                     |        ✅       |            ✅           |            ✅           |              ✅              |                    |

### Sparse or Inttermitent

Suited for series with very few non-zero observations

| Model                                                                                                 | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | Exogenous features |
| :---------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: | :-------------------------: | :----------------: |
| [ADIDA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#adida)                       |        ✅       |                        |            ✅           |              ✅              |                    |
| [CrostonClassic](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonclassic)     |        ✅       |                        |            ✅           |              ✅              |                    |
| [CrostonOptimized](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonoptimized) |        ✅       |                        |            ✅           |              ✅              |                    |
| [CrostonSBA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#crostonsba)             |        ✅       |                        |            ✅           |              ✅              |                    |
| [IMAPA](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#imapa)                       |        ✅       |                        |            ✅           |              ✅              |                    |
| [TSB](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#tsb)                           |        ✅       |                        |            ✅           |              ✅              |                    |

## 🔨 How to contribute

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

## Citing

```bibtex theme={null}
@misc{garza2022statsforecast,
    author={Azul Garza, Max Mergenthaler Canseco, Cristian Challú, Kin G. Olivares},
    title = {{StatsForecast}: Lightning fast forecasting with statistical and econometric models},
    year={2022},
    howpublished={{PyCon} Salt Lake City, Utah, US 2022},
    url={https://github.com/Nixtla/statsforecast}
}
```

## Contributors ✨

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

<table>
  <tbody>
    <tr>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AzulGarza"><img src="https://avatars.githubusercontent.com/u/10517170?v=4?s=100" width="100px;" alt="azul" /><br /><sub><b>azul</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=AzulGarza" title="Code">💻</a> <a href="#maintenance-AzulGarza" title="Maintenance">🚧</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="José Morales" /><br /><sub><b>José Morales</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=jmoralez" title="Code">💻</a> <a href="#maintenance-jmoralez" title="Maintenance">🚧</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">💻</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">🐛</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">🤔</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">💬</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">💬</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">💻</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">💻</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">🤔</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">🤔</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 Gillißen" /><br /><sub><b>Philip Gillißen</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=guerda" title="Code">💻</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">🐛</a> <a href="https://github.com/Nixtla/statsforecast/commits?author=shagn" title="Documentation">📖</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">💻</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">🐛</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">📖</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 García " /><br /><sub><b>Mariana Menchero García </b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=MMenchero" title="Code">💻</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">🐛</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">🐛</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">💻</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">💻</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">💻</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">📖</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 Cárdenas Bolaño" /><br /><sub><b>Nelson Cárdenas Bolaño</b></sub></a><br /><a href="https://github.com/Nixtla/statsforecast/commits?author=nelsoncardenas" title="Documentation">📖</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">💻</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">💻</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">💻</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">💻</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">📖</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">📖</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">📖</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">💻</a></td>
    </tr>
  </tbody>
</table>

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