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
Commit History
Yesterday
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Fix the tag in the SageMaker notebook link
Verified signatureThe link pins the notebook to tag 2.2.2, and the repository tags that release as v2.2.2, so it returns 404. The pinned version is kept and only the tag gains its prefix. The notebook path is unchanged and the URL returns 200.96c447a
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Update deployment guide
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Fix broken link in README
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Add update for new SageMaker deployment guide (#3)
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Remove `transformers` from model tags (#2)
Verified signature- Remove `transformers` from model tags (445fac389fda1880c915f888a959299159de71de) Co-authored-by: Lucain Pouget <Wauplin@users.noreply.huggingface.co>e19c427
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