Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
pretrained models
foundation models
time series foundation models
time-series
Instructions to use amazon/chronos-t5-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-t5-mini with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-t5-mini", 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
pr-branch-1 (#1)
Browse files- try modifying readme (f67560a937cc96b3d016e825b3c24a8ae1a97b49)
- edit readme again (3f178b47b280e824f9f8e9798a0f038f28584b4c)
Co-authored-by: Caner Turkmen <canerturkmen@users.noreply.huggingface.co>
README.md
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license: apache-2.0
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license: apache-2.0
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tags:
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- time series
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- forecasting
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- pretrained models
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- foundation models
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- time series foundation models
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- time-series
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