> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/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#mean-absolute-error-mae)       | [**MAPE**](../../losses.pytorch.html#mean-absolute-percentage-error-mape) | [**MASE**](../../losses.pytorch.html#mean-absolute-scaled-error-mase) | [**Huber**](../losses.pytorch.html#huber-loss)                |
| [**MSE**](../../losses.pytorch.html#mean-squared-error-mse)        | [**sMAPE**](../../losses.pytorch.html#symmetric-mape-smape)               |                                                                       | [**Tukey**](../../losses.pytorch.html#tukey-loss)             |
| [**RMSE**](../../losses.pytorch.html#root-mean-squared-error-rmse) |                                                                           |                                                                       | [**HuberMQLoss**](../../losses.pytorch.html#huberized-mqloss) |

## Probabilistic losses

| Parametric Probabilities                                                  | Non-Parametric Probabilities                                        |
| :------------------------------------------------------------------------ | :------------------------------------------------------------------ |
| [**Normal**](../../losses.pytorch.html#distributionloss)                  | [**QuantileLoss**](../../losses.pytorch.html#quantile-loss)         |
| [**StudenT**](../../losses.pytorch.html#distributionloss)                 | [**MQLoss**](../../losses.pytorch.html#multi-quantile-loss-mqloss)  |
| [**Poisson**](../../losses.pytorch.html#distributionloss)                 | [**HuberQLoss**](../../losses.pytorch.html#huberized-quantile-loss) |
| [**Negative Binomial**](../../losses.pytorch.html#distributionloss)       | [**HuberMQLoss**](../../losses.pytorch.html#huberized-mqloss)       |
| [**Tweedie**](../../losses.pytorch.html#distributionloss)                 | [**IQLoss**](../../losses.pytorch.html#iqloss)                      |
| [**PMM**](../../losses.pytorch.html#poisson-mixture-mesh-pmm)             | [**HuberIQLoss**](../../losses.pytorch.html#huberized-iqloss)       |
| [**GMM**](../../losses.pytorch.html#gaussian-mixture-mesh-gmm)            | [**ISQF**](../../losses.pytorch.html#isqf)                          |
| [**NBMM**](../../losses.pytorch.html#negative-binomial-mixture-mesh-nbmm) |                                                                     |