Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
foundation models
pretrained models
Instructions to use autogluon/chronos-2-synth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use autogluon/chronos-2-synth with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("autogluon/chronos-2-synth", 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
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README.md
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# Chronos-2-Synth
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This is a variant of the Chronos-2 model which has _only_ been trained on synthetic univariate and multivariate data. For details on the Chronos-2 model, please refer to https://huggingface.co/
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## Citation
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# Chronos-2-Synth
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This is a variant of the Chronos-2 model which has _only_ been trained on synthetic univariate and multivariate data. For usage and details on the Chronos-2 model, please refer to https://huggingface.co/autogluon/chronos-2.
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## Citation
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