Time Series Forecasting
Chronos
Safetensors
t5
time-series
forecasting
chronos
chronos-2
quantization
torchao
int8
8-bit precision
Instructions to use oxfrug/chronos-2-int8-torchao with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use oxfrug/chronos-2-int8-torchao with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("oxfrug/chronos-2-int8-torchao", 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:
- 79b37aefd01b7c76adba0465dd93f92e76b05076c39e86eeefe660ca75c18735
- Size of remote file:
- 131 MB
- SHA256:
- 09be1ae85b40fee3fbdc2d05d66bbf4efc831f0b1a01c2dbce6aabced0bcc9e4
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