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Add Reverso foundation model for zero-shot time series forecasting #3061

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Add Reverso foundation model for zero-shot time series forecasting #3061

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Add Reverso foundation model for zero-shot time series forecasting
shinfxh Apr 6, 2026
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add chunking. recommended chunk sizes: nano 512, small 128, base 64
shinfxh Apr 6, 2026
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fix changelog
shinfxh Apr 6, 2026
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shinfxh Apr 6, 2026
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shinfxh Apr 6, 2026
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update documentation
shinfxh Apr 7, 2026
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -9,6 +9,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co

### For users of the library:

- Added `ReversoModel`, a new foundation model for zero-shot time series forecasting. Reverso is a highly parameter-efficient model (200K-2.6M params) that matches accuracy of models 100x its size. [#3034](https://github.com/unit8co/darts/issues/3034) by [Xinghong Fu](https://github.com/shinfxh).
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@daidahao daidahao Apr 12, 2026

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Suggested change
- Added `ReversoModel`, a new foundation model for zero-shot time series forecasting. Reverso is a highly parameter-efficient model (200K-2.6M params) that matches accuracy of models 100x its size. [#3034](https://github.com/unit8co/darts/issues/3034) by [Xinghong Fu](https://github.com/shinfxh).
- Added new forecasting model `ReversoModel`: a family of highly parameter-efficient (200K-2.6M) foundation models that matches accuracy of models 100x their sizes. It supports univariate, multivariate, and multiple time series forecasting without training and can be fine-tuned on your own data. [#3034](https://github.com/unit8co/darts/issues/3034) by [Xinghong Fu](https://github.com/shinfxh).

- Added native multi-quantile support for `CatBoostModel` by using CatBoost’s `MultiQuantile` loss for faster training and inference. Set `likelihood="multiquantile"` to enable this feature. [#3032](https://github.com/unit8co/darts/pull/3032) by [Zhihao Dai](https://github.com/daidahao)

**Fixed**
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -277,6 +277,7 @@ Here's a breakdown of the forecasting models currently implemented in Darts. Our
| **Foundation Models**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): No training required | | | | | |
| [Chronos2Model](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.chronos2_model.html#darts.models.forecasting.chronos2_model.Chronos2Model) | [Chronos-2 report](https://arxiv.org/abs/2510.15821), [Amazon blog post](https://www.amazon.science/blog/introducing-chronos-2-from-univariate-to-universal-forecasting) | ✅ ✅ | ✅ ✅ 🔴 | ✅ ✅ | ✅ |
| [TimesFM2p5Model](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.timesfm2p5_model.html#darts.models.forecasting.timesfm2p5_model.TimesFM2p5Model) | [TimesFM 1.0 paper](https://arxiv.org/abs/2310.10688), [Google blog post](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting) | ✅ ✅ | 🔴 🔴 🔴 | ✅ ✅ | ✅ |
| [ReversoModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.reverso_model.html#darts.models.forecasting.reverso_model.ReversoModel) | [Reverso paper](https://arxiv.org/abs/2602.17634), [GitHub](https://github.com/shinfxh/reverso) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | ✅ |
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Reverso supports multivariate.

| **Ensemble Models**<br/>([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): Model support is dependent on ensembled forecasting models and the ensemble model itself | | | | | |
| [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
| [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ |
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5 changes: 4 additions & 1 deletion darts/models/__init__.py
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Expand Up @@ -5,7 +5,7 @@
A comprehensive collection of forecasting and filtering models, including baseline models
(NaiveSeasonal, NaiveMovingAverage, ...), statistical models (ARIMA, exponential smoothing, ...),
machine learning models (LightGBM, CatBoost, sklearn-based, ...), neural network models (RNN,
N-BEATS, TiDE...), and foundation models (Chronos-2, TimesFM 2.5).
N-BEATS, TiDE...), and foundation models (Chronos-2, Reverso, TimesFM 2.5).
"""

from darts.logging import get_logger
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try:
from darts.models.forecasting.chronos2_model import Chronos2Model
from darts.models.forecasting.reverso_model import ReversoModel
from darts.models.forecasting.timesfm2p5_model import TimesFM2p5Model
except ModuleNotFoundError:
Chronos2Model = NotImportedModule(module_name="(Py)Torch", warn=False)
ReversoModel = NotImportedModule(module_name="(Py)Torch", warn=False)
TimesFM2p5Model = NotImportedModule(module_name="(Py)Torch", warn=False)

try:
Expand Down Expand Up @@ -210,6 +212,7 @@
"ConformalNaiveModel",
"ConformalQRModel",
"Chronos2Model",
"ReversoModel",
"TimesFM2p5Model",
"NeuralForecastModel",
]
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