Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
pretrained models
foundation models
time series foundation models
time-series
Instructions to use CollectionStudio/chronos-bolt-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use CollectionStudio/chronos-bolt-tiny with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("CollectionStudio/chronos-bolt-tiny", 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
- Xet hash:
- 44541c0c6b8ba4b20ae20ee1dd9d0eb2228099abc6ec0f9562a6ef1a6055ce17
- Size of remote file:
- 34.6 MB
- SHA256:
- 75068728d376d2bec670379eeef4bfb4d24c0cfe24d957451f8d19b447030a32
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