> ## 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.

# Optimization Objectives

NeuralForecast is a highly modular framework capable of augmenting a
wide variety of robust neural network architectures with different point
or probability outputs as defined by their optimization objectives.

## Point losses

| Scale-Dependent                            | Percentage-Errors                            | Scale-Independent                          | Robust                                                   |
| :----------------------------------------- | :------------------------------------------- | :----------------------------------------- | :------------------------------------------------------- |
| [**MAE**](../../losses.pytorch.html#mae)   | [**MAPE**](../../losses.pytorch.html#mape)   | [**MASE**](../../losses.pytorch.html#mase) | [**Huber**](../../losses.pytorch.html#huber-loss)        |
| [**MSE**](../../losses.pytorch.html#mse)   | [**sMAPE**](../../losses.pytorch.html#smape) |                                            | [**Tukey**](../../losses.pytorch.html#tukeyloss)         |
| [**RMSE**](../../losses.pytorch.html#rmse) |                                              |                                            | [**HuberMQLoss**](../../losses.pytorch.html#hubermqloss) |

## Probabilistic losses

| Parametric Probabilities                                            | Non-Parametric Probabilities                               |
| :------------------------------------------------------------------ | :--------------------------------------------------------- |
| [**Normal**](../../losses.pytorch.html#distributionloss)            | [**QuantileLoss**](../../losses.pytorch.html#quantileloss) |
| [**StudenT**](../../losses.pytorch.html#distributionloss)           | [**MQLoss**](../../losses.pytorch.html#mqloss)             |
| [**Poisson**](../../losses.pytorch.html#distributionloss)           | [**HuberQLoss**](../../losses.pytorch.html#huberiqloss)    |
| [**Negative Binomial**](../../losses.pytorch.html#distributionloss) | [**HuberMQLoss**](../../losses.pytorch.html#hubermqloss)   |
| [**Tweedie**](../../losses.pytorch.html#distributionloss)           | [**IQLoss**](../../losses.pytorch.html#iqloss)             |
| [**PMM**](../../losses.pytorch.html#pmm)                            | [**HuberIQLoss**](../../losses.pytorch.html#huberiqloss)   |
| [**GMM**](../../losses.pytorch.html#gmm)                            | [**ISQF**](../../losses.pytorch.html#isqf)                 |
| [**NBMM**](../../losses.pytorch.html#nbmm)                          |                                                            |