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Update models in the README #946

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May 28, 2024
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Update models in the README #946

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c4cdad0
Update models in the README
candalfigomoro Mar 29, 2024
9400ded
Add more models
candalfigomoro Apr 23, 2024
52792e2
Add Time-LLM
candalfigomoro Apr 23, 2024
3e96153
Add TiDE
candalfigomoro May 7, 2024
207f298
add whitespaces
candalfigomoro May 7, 2024
d9af6f7
add whitespaces
candalfigomoro May 7, 2024
db5822a
Add DeepNPTS
candalfigomoro May 8, 2024
8a6832a
Merge branch 'main' into patch-1
elephaint May 8, 2024
5516c29
add tsmixerx to lists and fix multivariate
candalfigomoro May 8, 2024
7c095f7
Merge branch 'main' into patch-1
elephaint May 28, 2024
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13 changes: 11 additions & 2 deletions README.md
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Expand Up @@ -15,7 +15,7 @@
[![All Contributors](https://img.shields.io/badge/all_contributors-11-orange.svg?style=flat-square)](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->

**NeuralForecast** offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like `RNN` to the latest transformers: `MLP`, `LSTM`, `GRU`,`RNN`,`TCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `DLinear`, `NLinear`,`TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`,`StemGNN`, and `TimesNet`.
**NeuralForecast** offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like RNNs to the latest transformers: `MLP`, `LSTM`, `GRU`, `RNN`, `TCN`, `TimesNet`, `BiTCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `TiDE`, `DeepNPTS`, `TSMixer`, `MLPMultivariate`, `DLinear`, `NLinear`, `TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`, `iTransformer`, `StemGNN`, and `TimeLLM`.
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</div>

## Installation
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## Features

* Fast and accurate implementations of `MLP`, `LSTM`, `GRU`,`RNN`,`TCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `DLinear`,`TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`,`StemGNN`, and `TimesNet`. See the entire [collection here](https://nixtla.github.io/neuralforecast/models.html).
* Fast and accurate implementations of `MLP`, `LSTM`, `GRU`, `RNN`, `TCN`, `TimesNet`, `BiTCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `TiDE`, `DeepNPTS`, `TSMixer`, `MLPMultivariate`, `DLinear`, `NLinear`, `TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`, `iTransformer`, `StemGNN`, and `TimeLLM`. See the entire [collection here](https://nixtla.github.io/neuralforecast/models.html).
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* Support for exogenous variables and static covariates.
* Interpretability methods for trend, seasonality and exogenous components.
* Probabilistic Forecasting with adapters for quantile losses and parametric distributions.
Expand Down Expand Up @@ -105,17 +105,26 @@ The [documentation page](https://nixtla.github.io/neuralforecast/) contains all
|[DeepAR](https://nixtlaverse.nixtla.io/neuralforecast/models.deepar.html)| RNN | recurrent ||✅|✅|✅|
|[TCN](https://nixtlaverse.nixtla.io/neuralforecast/models.tcn.html)| CNN | recurrent |✅|✅|✅|✅|
|[TimesNet](https://nixtlaverse.nixtla.io/neuralforecast/models.timesnet.html)| CNN | windows |✅|✅||✅|
|[BiTCN](https://nixtlaverse.nixtla.io/neuralforecast/models.bitcn.html)| CNN | windows |✅|✅|✅|✅|
|[DLinear](https://nixtlaverse.nixtla.io/neuralforecast/models.dlinear.html)| Linear | windows |✅|✅||✅|
|[NLinear](https://nixtlaverse.nixtla.io/neuralforecast/models.nlinear.html)| Linear | windows |✅|✅||✅|
|[MLP](https://nixtlaverse.nixtla.io/neuralforecast/models.mlp.html)| MLP | windows |✅|✅|✅|✅|
|[NBEATS](https://nixtlaverse.nixtla.io/neuralforecast/models.nbeats.html)| MLP | windows |✅|✅||✅|
|[NBEATSx](https://nixtlaverse.nixtla.io/neuralforecast/models.nbeatsx.html)| MLP | windows |✅|✅|✅|✅|
|[NHITS](https://nixtlaverse.nixtla.io/neuralforecast/models.nhits.html)| MLP | windows |✅|✅|✅|✅|
|[TiDE](https://nixtlaverse.nixtla.io/neuralforecast/models.tide.html)| MLP | windows |✅|✅|✅|✅|
|[DeepNPTS](https://nixtlaverse.nixtla.io/neuralforecast/models.deepnpts.html)| MLP | windows |✅||✅|✅|
|[TSMixer](https://nixtlaverse.nixtla.io/neuralforecast/models.tsmixer.html)| MLP | multivariate |✅|✅||✅|
|[TSMixerx](https://nixtlaverse.nixtla.io/neuralforecast/models.tsmixerx.html)| MLP | multivariate |✅|✅|✅|✅|
|[MLPMultivariate](https://nixtlaverse.nixtla.io/neuralforecast/models.mlpmultivariate.html)| MLP | multivariate |✅|✅|✅|✅|
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|[TFT](https://nixtlaverse.nixtla.io/neuralforecast/models.tft.html)| Transformer | windows |✅|✅|✅|✅|
|[Transformer](https://nixtlaverse.nixtla.io/neuralforecast/models.vanillatransformer.html)| Transformer | windows |✅|✅|✅|✅|
|[Informer](https://nixtlaverse.nixtla.io/neuralforecast/models.informer.html)| Transformer | windows |✅|✅|✅|✅|
|[Autoformer](https://nixtlaverse.nixtla.io/neuralforecast/models.autoformer.html)| Transformer | windows |✅|✅|✅|✅|
|[FEDFormer](https://nixtlaverse.nixtla.io/neuralforecast/models.fedformer.html)| Transformer | windows |✅|✅|✅|✅|
|[PatchTST](https://nixtlaverse.nixtla.io/neuralforecast/models.patchtst.html)| Transformer | windows |✅|✅||✅|
|[Time-LLM](https://nixtlaverse.nixtla.io/neuralforecast/models.timellm.html)| Transformer | windows |✅|✅|||
|[iTransformer](https://nixtlaverse.nixtla.io/neuralforecast/models.itransformer.html)| Transformer | multivariate |✅|✅||✅|
|[StemGNN](https://nixtlaverse.nixtla.io/neuralforecast/models.stemgnn.html)| GNN | multivariate |✅|||✅|

Missing a model? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A)
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