Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
pretrained models
foundation models
time series foundation models
time-series
Instructions to use amazon/chronos-bolt-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-bolt-small with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-bolt-small", 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
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Enhance model card with library, paper link, and GitHub reference
Verified signatureThis PR enriches the model card for `chronos-bolt-small` by: - Adding the `library_name: chronos-forecasting` to enable the interactive "How to use" widget on the Hub, providing a seamless experience for users. This choice is based on the explicit usage examples provided in the model card and GitHub README, which demonstrate inference with `chronos.BaseChronosPipeline` from the `chronos-forecasting` package. - Linking directly to the official Hugging Face paper page for [Chronos-2: From Univariate to Universal Forecasting](https://huggingface.co/papers/2510.15821) as the primary associated paper, and including its BibTeX citation in the "Citation" section. - Adding a direct link to the GitHub repository (https://github.com/amazon-science/chronos-forecasting) for easy access to the source code and examples. These updates improve discoverability and provide clearer guidance for users.c0c1c21
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