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add exogenous features support to models table #714

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add exogenous features support to models table #714

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64 changes: 32 additions & 32 deletions README.md
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Expand Up @@ -109,77 +109,77 @@ Missing something? Please open an issue or write us in [![Slack](https://img.shi
### 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 |
|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|
|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|
|[AutoARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#autoarima)|✅|✅|✅|✅|✅|
|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|✅|✅|✅|✅|✅|
|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|✅|✅|✅|✅|✅|
|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta)|✅|✅|✅|✅|✅|
|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|✅|✅|✅|✅||
|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|✅|✅|✅|✅||
|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta)|✅|✅|✅|✅||

### ARIMA Family
These models exploit the existing autocorrelations in the time series.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|
|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|
|[ARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#arima)|✅|✅|✅|✅|✅|
|[AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#autoregressive)|✅|✅|✅|✅|✅|

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

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

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

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

### GARCH and ARCH Models
Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|
|[GARCH](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|✅|✅|✅|✅|✅|
|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch)|✅|✅|✅|✅|✅|
|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features|
|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:|
|[GARCH](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|✅|✅|✅|✅||
|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch)|✅|✅|✅|✅||


### Baseline Models
Classical models for establishing baseline.

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

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

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


### Sparse or Intermittent
Suited for series with very few non-zero observations

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