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Align UR2CUTE integration with upstream release (auto-threshold, masked losses, tests) #1410

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Align UR2CUTE integration with upstream release (auto-threshold, masked losses, tests) #1410

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24152a8
Add UR2CUTE model for intermittent demand forecasting
Oct 24, 2025
ef1cf66
Merge branch 'main' into main
FH-Prevail Nov 13, 2025
d6dcec3
Fix bugs
Nov 15, 2025
6d184f9
Merge branch 'Nixtla:main' into main
FH-Prevail Nov 18, 2025
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5 changes: 4 additions & 1 deletion .gitignore
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Expand Up @@ -42,4 +42,7 @@ docs/mintlify/examples
nbs/_extensions
.quarto

*.png
*.png
PR_READY_SUMMARY.md
PR_DESCRIPTION.md
.claude/settings.local.json
2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -18,7 +18,7 @@
[![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 RNNs to the latest transformers: `MLP`, `LSTM`, `GRU`, `RNN`, `TCN`, `TimesNet`, `BiTCN`, `DeepAR`, `NBEATS`, `NBEATSx`, `NHITS`, `TiDE`, `DeepNPTS`, `TSMixer`, `TSMixerx`, `MLPMultivariate`, `DLinear`, `NLinear`, `TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`, `iTransformer`, `StemGNN`, and `TimeLLM`.
**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`, `TSMixerx`, `MLPMultivariate`, `DLinear`, `NLinear`, `TFT`, `Informer`, `AutoFormer`, `FedFormer`, `PatchTST`, `iTransformer`, `StemGNN`, `TimeLLM`, and `UR2CUTE`.
</div>

## Installation
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3 changes: 2 additions & 1 deletion neuralforecast/models/__init__.py
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'TFT', 'VanillaTransformer', 'Informer', 'Autoformer', 'PatchTST', 'FEDformer',
'StemGNN', 'HINT', 'TimesNet', 'TimeLLM', 'TSMixer', 'TSMixerx', 'MLPMultivariate',
'iTransformer', 'BiTCN', 'TiDE', 'DeepNPTS', 'SOFTS', 'TimeMixer', 'KAN', 'RMoK',
'TimeXer', 'xLSTM'
'TimeXer', 'xLSTM', 'UR2CUTE'
]

from .rnn import RNN
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from .rmok import RMoK
from .timexer import TimeXer
from .xlstm import xLSTM
from .ur2cute import UR2CUTE
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