> ## 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.

# Forecasting Models

NeuralForecast currently offers the following models.

| Model<sup>1</sup>    | AutoModel<sup>2</sup>    | Family<sup>3</sup> | Univariate / Multivariate<sup>4</sup> | Forecast Type<sup>5</sup> | Exogenous<sup>6</sup> |
| :------------------- | :----------------------- | :----------------- | :------------------------------------ | :------------------------ | :-------------------- |
| `Autoformer`         | `AutoAutoformer`         | Transformer        | Univariate                            | Direct                    | F                     |
| `BiTCN`              | `AutoBiTCN`              | CNN                | Univariate                            | Direct                    | F/H/S                 |
| `DeepAR`             | `AutoDeepAR`             | RNN                | Univariate                            | Direct                    | F/S                   |
| `DeepNPTS`           | `AutoDeepNPTS`           | MLP                | Univariate                            | Direct                    | F/H/S                 |
| `DilatedRNN`         | `AutoDilatedRNN`         | RNN                | Univariate                            | Direct                    | F/H/S                 |
| `FEDformer`          | `AutoFEDformer`          | Transformer        | Univariate                            | Direct                    | F                     |
| `GRU`                | `AutoGRU`                | RNN                | Univariate                            | Both<sup>8</sup>          | F/H/S                 |
| `HINT`               | `AutoHINT`               | Any<sup>7</sup>    | Both<sup>7</sup>                      | Both<sup>7</sup>          | F/H/S                 |
| `Informer`           | `AutoInformer`           | Transformer        | Univariate                            | Direct                    | F                     |
| `iTransformer`       | `AutoiTransformer`       | Transformer        | Multivariate                          | Direct                    | -                     |
| `KAN`                | `AutoKAN`                | KAN                | Univariate                            | Direct                    | F/H/S                 |
| `LSTM`               | `AutoLSTM`               | RNN                | Univariate                            | Both<sup>8</sup>          | F/H/S                 |
| `MLP`                | `AutoMLP`                | MLP                | Univariate                            | Direct                    | F/H/S                 |
| `MLPMultivariate`    | `AutoMLPMultivariate`    | MLP                | Multivariate                          | Direct                    | F/H/S                 |
| `NBEATS`             | `AutoNBEATS`             | MLP                | Univariate                            | Direct                    | -                     |
| `NBEATSx`            | `AutoNBEATSx`            | MLP                | Univariate                            | Direct                    | F/H/S                 |
| `NHITS`              | `AutoNHITS`              | MLP                | Univariate                            | Direct                    | F/H/S                 |
| `NLinear`            | `AutoNLinear`            | MLP                | Univariate                            | Direct                    | -                     |
| `PatchTST`           | `AutoPatchTST`           | Transformer        | Univariate                            | Direct                    | -                     |
| `RMoK`               | `AutoRMoK`               | KAN                | Multivariate                          | Direct                    | -                     |
| `RNN`                | `AutoRNN`                | RNN                | Univariate                            | Both<sup>8</sup>          | F/H/S                 |
| `SOFTS`              | `AutoSOFTS`              | MLP                | Multivariate                          | Direct                    | -                     |
| `StemGNN`            | `AutoStemGNN`            | GNN                | Multivariate                          | Direct                    | -                     |
| `TCN`                | `AutoTCN`                | CNN                | Univariate                            | Direct                    | F/H/S                 |
| `TFT`                | `AutoTFT`                | Transformer        | Univariate                            | Direct                    | F/H/S                 |
| `TiDE`               | `AutoTiDE`               | MLP                | Univariate                            | Direct                    | F/H/S                 |
| `TimeMixer`          | `AutoTimeMixer`          | MLP                | Multivariate                          | Direct                    | -                     |
| `TimeLLM`            | -                        | LLM                | Univariate                            | Direct                    | -                     |
| `TimesNet`           | `AutoTimesNet`           | CNN                | Univariate                            | Direct                    | F                     |
| `TimeXer`            | `AutoTimeXer`            | Transformer        | Multivariate                          | Direct                    | H/S                   |
| `TSMixer`            | `AutoTSMixer`            | MLP                | Multivariate                          | Direct                    | -                     |
| `TSMixerx`           | `AutoTSMixerx`           | MLP                | Multivariate                          | Direct                    | F/H/S                 |
| `VanillaTransformer` | `AutoVanillaTransformer` | Transformer        | Univariate                            | Direct                    | F                     |
| `XLinear`            | `AutoXLinear`            | MLP                | Multivariate                          | Direct                    | F/H/S                 |
| `xLSTM`              | `AutoxLSTM`              | mLSTM              | Univariate                            | Direct                    | F/H/S                 |

1. **Model**: The model name.
2. **AutoModel**: NeuralForecast offers most models also in an Auto\*
   version, in which the hyperparameters of the underlying model are
   automatically optimized and the best-performing model for a
   validation set is selected. The optimization methods include grid
   search, random search, and Bayesian optimization.
3. **Family**: The main neural network architecture underpinning the
   model.
4. **Univariate / Multivariate**: A multivariate model explicitly
   models the interactions between multiple time series in a dataset
   and will provide predictions for multiple time series concurrently.
   In contrast, a univariate model trained on multiple time series
   implicitly models interactions between multiple time series and
   provides predictions for single time series concurrently.
   Multivariate models are typically computationally expensive and
   empirically do not necessarily offer better forecasting performance
   compared to using a univariate model.
5. **Forecast Type**: Direct forecast models are models that produce
   all steps in the forecast horizon at once. In contrast, recursive
   forecast models predict one-step ahead, and subsequently use the
   prediction to compute the next step in the forecast horizon, and so
   forth. Direct forecast models typically suffer less from bias and
   variance propagation as compared to recursive forecast models,
   whereas recursive models can be computationally less expensive.
6. **Exogenous**: Whether the model accepts exogenous variables. This
   can be exogenous variables that contain information about the past
   and future (F), about the past only (*historical*, H), or that
   contain static information (*static*, S).
7. **HINT** is a modular framework that can combine any type of neural
   architecture with task-specialized mixture probability and advanced
   hierarchical reconciliation strategies.
8. Models that can produce forecasts recursively and direct. For
   example, the RNN model uses an RNN to encode the past sequence, and
   subsequently the user can choose between producing forecasts
   recursively using the RNN or direct using an MLP that uses the
   encoded sequence as input. The models feature an `recursive=False`
   feature that sets how they produce forecasts.