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‎statsforecast/docs/contribute/step-by-step.html.mdx‎

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# Step-by-step Contribution Guide
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> This document contains instructions for collaborating on the different
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> libraries of Nixtla.
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---
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title: Step-by-step Contribution Guide
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description: This document contains instructions for collaborating on the different libraries of Nixtla.
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---
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Sometimes, diving into a new technology can be challenging and
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overwhelming. We’ve been there too, and we’re more than ready to assist

‎statsforecast/docs/contribute/techstack.html.mdx‎

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# Contributing Code to Nixtla Development
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---
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title: Contributing Code to Nixtla Development
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description: A guide on the technical skills and tools needed to contribute code to the Nixtla project.
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---
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Curious about the skills required to contribute to the Nixtla project?
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‎statsforecast/src/core/models_intro.html.mdx‎

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@@ -8,66 +8,66 @@ Automatic forecasting tools search for the best parameters and select
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the best possible model for a series of time series. These tools are
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useful for large collections of univariate time series.
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`AutoARIMA`](../../models.html#autoarima) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoETS`](../../models.html#autoets) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoCES`](../../models.html#autoces) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoTheta`](../../models.html#autotheta) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoARIMA`](models.html#autoarima) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoETS`](models.html#autoets) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoCES`](models.html#autoces) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoTheta`](models.html#autotheta) | ✅ | ✅ | ✅ | ✅ |
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## ARIMA Family
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These models exploit the existing autocorrelations in the time series.
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`ARIMA`](../../models.html#arima) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoRegressive`](../../models.html#autoregressive) | ✅ | ✅ | ✅ | ✅ |
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| [`ARIMA`](models.html#arima) | ✅ | ✅ | ✅ | ✅ |
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| [`AutoRegressive`](models.html#autoregressive) | ✅ | ✅ | ✅ | ✅ |
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## Theta Family
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Fit two theta lines to a deseasonalized time series, using different
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techniques to obtain and combine the two theta lines to produce the
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final forecasts.
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`Theta`](../../models.html#theta) | ✅ | ✅ | ✅ | ✅ |
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| [`OptimizedTheta`](../../models.html#optimizedtheta) | ✅ | ✅ | ✅ | ✅ |
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| [`DynamicTheta`](../../models.html#dynamictheta) | ✅ | ✅ | ✅ | ✅ |
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| [`DynamicOptimizedTheta`](../../models.html#dynamicoptimizedtheta) | ✅ | ✅ | ✅ | ✅ |
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| [`Theta`](models.html#theta) | ✅ | ✅ | ✅ | ✅ |
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| [`OptimizedTheta`](models.html#optimizedtheta) | ✅ | ✅ | ✅ | ✅ |
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| [`DynamicTheta`](models.html#dynamictheta) | ✅ | ✅ | ✅ | ✅ |
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| [`DynamicOptimizedTheta`](models.html#dynamicoptimizedtheta) | ✅ | ✅ | ✅ | ✅ |
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## Multiple Seasonalities
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Suited for signals with more than one clear seasonality. Useful for
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low-frequency data like electricity and logs.
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`MSTL`](../../models.html#mstl) | ✅ | ✅ | ✅ | ✅ |
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| [`MSTL`](models.html#mstl) | ✅ | ✅ | ✅ | ✅ |
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## GARCH and ARCH Models
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Suited for modeling time series that exhibit non-constant volatility
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over time. The ARCH model is a particular case of GARCH.
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`GARCH`](../../models.html#garch) | ✅ | ✅ | ✅ | ✅ |
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| [`ARCH`](../../models.html#arch) | ✅ | ✅ | ✅ | ✅ |
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| [`GARCH`](models.html#garch) | ✅ | ✅ | ✅ | ✅ |
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| [`ARCH`](models.html#arch) | ✅ | ✅ | ✅ | ✅ |
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## Baseline Models
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Classical models for establishing baseline.
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`HistoricAverage`](../../models.html#historicaverage) | ✅ | ✅ | ✅ | ✅ |
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| [`Naive`](../../models.html#naive) | ✅ | ✅ | ✅ | ✅ |
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| [`RandomWalkWithDrift`](../../models.html#randomwalkwithdrift) | ✅ | ✅ | ✅ | ✅ |
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| [`SeasonalNaive`](../../models.html#seasonalnaive) | ✅ | ✅ | ✅ | ✅ |
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| [`WindowAverage`](../../models.html#windowaverage) | ✅ | | | |
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| [`SeasonalWindowAverage`](../../models.html#seasonalwindowaverage) | ✅ | | | |
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| [`HistoricAverage`](models.html#historicaverage) | ✅ | ✅ | ✅ | ✅ |
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| [`Naive`](models.html#naive) | ✅ | ✅ | ✅ | ✅ |
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| [`RandomWalkWithDrift`](models.html#randomwalkwithdrift) | ✅ | ✅ | ✅ | ✅ |
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| [`SeasonalNaive`](models.html#seasonalnaive) | ✅ | ✅ | ✅ | ✅ |
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| [`WindowAverage`](models.html#windowaverage) | ✅ | | | |
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| [`SeasonalWindowAverage`](models.html#seasonalwindowaverage) | ✅ | | | |
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## Exponential Smoothing
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and/or seasonality. Use the `SimpleExponential` family for data with no
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clear trend or seasonality.
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`SimpleExponentialSmoothing`](../../models.html#simpleexponentialsmoothing) | ✅ | | | |
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| [`SimpleExponentialSmoothingOptimized`](../../models.html#simpleexponentialsmoothingoptimized) | ✅ | | | |
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| [`Holt`](../../models.html#holt) | ✅ | ✅ | ✅ | ✅ |
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| [`HoltWinters`](../../models.html#holtwinters) | ✅ | ✅ | ✅ | ✅ |
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| [`SimpleExponentialSmoothing`](models.html#simpleexponentialsmoothing) | ✅ | | | |
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| [`SimpleExponentialSmoothingOptimized`](models.html#simpleexponentialsmoothingoptimized) | ✅ | | | |
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| [`Holt`](models.html#holt) | ✅ | ✅ | ✅ | ✅ |
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| [`HoltWinters`](models.html#holtwinters) | ✅ | ✅ | ✅ | ✅ |
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## Sparse or Intermittent
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Suited for series with very few non-zero observations
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| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
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|:-----|:----------:|:----------------:|:---------------:|:--------------------:|
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| [`ADIDA`](../../models.html#adida) | ✅ | | | |
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| [`CrostonClassic`](../../models.html#crostonclassic) | ✅ | | | |
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| [`CrostonOptimized`](../../models.html#crostonoptimized) | ✅ | | | |
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| [`CrostonSBA`](../../models.html#crostonsba) | ✅ | | | |
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| [`IMAPA`](../../models.html#imapa) | ✅ | | | |
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| [`TSB`](../../models.html#tsb) | ✅ | | | |
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| [`ADIDA`](models.html#adida) | ✅ | | | |
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| [`CrostonClassic`](models.html#crostonclassic) | ✅ | | | |
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| [`CrostonOptimized`](models.html#crostonoptimized) | ✅ | | | |
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| [`CrostonSBA`](models.html#crostonsba) | ✅ | | | |
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| [`IMAPA`](models.html#imapa) | ✅ | | | |
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| [`TSB`](models.html#tsb) | ✅ | | | |
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