Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
foundation models
pretrained models
Instructions to use amazon/chronos-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-2 with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cuda") # Load historical data context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv") # Generate predictions pred_df = pipeline.predict_df( context_df, prediction_length=36, # Number of steps to forecast quantile_levels=[0.1, 0.5, 0.9], # Quantiles for probabilistic forecast id_column="item_id", # Column identifying different time series timestamp_column="Month", # Column with datetime information target="#Passengers", # Column(s) with time series values to predict ) - Notebooks
- Google Colab
- Kaggle
Remove `transformers` from model tags
Browse filesAccording to [this comment](https://github.com/huggingface/huggingface.js/pull/1826#issuecomment-3488286125), Chronos-2 is not natively supported by `transformers`. To avoid the confusion, I suggest to remove it from the model card tags. Model won't be listed in the [transformers-compatible models](https://huggingface.co/models?library=transformers) and won't have the `transformers` snippet in "use this model".
README.md
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- forecasting
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- foundation models
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- pretrained models
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- safetensors
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paper:
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- https://arxiv.org/abs/2510.15821
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- forecasting
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- foundation models
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- pretrained models
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- safetensors
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paper:
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- https://arxiv.org/abs/2510.15821
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