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-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-t5-large with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-t5-large", 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
Fix CONTRIBUTING reference
#1
by ZennyKenny - opened
README.md
CHANGED
|
@@ -97,7 +97,7 @@ If you find Chronos models useful for your research, please consider citing the
|
|
| 97 |
|
| 98 |
## Security
|
| 99 |
|
| 100 |
-
See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
|
| 101 |
|
| 102 |
## License
|
| 103 |
|
|
|
|
| 97 |
|
| 98 |
## Security
|
| 99 |
|
| 100 |
+
See [CONTRIBUTING](https://github.com/amazon-science/chronos-forecasting/blob/main/CONTRIBUTING.md#security-issue-notifications) for more information.
|
| 101 |
|
| 102 |
## License
|
| 103 |
|