ServiceNow/CAF_7M

CAF-7M is a semi-synthetic dataset to train or to test Context-Aided Forecasting models. This dataset was introduced in Overcoming the Modality Gap in Context-Aided Forecasting. CAF-7M was created by augmenting time series data from autogluon/chronos_datasets with informative contexts generated using a quantized version of Llama3.3-70B-Instruct. The dataset contains 7'433'239 training samples and 904 testing samples. The testing samples have been further filtered to only keep those where GPT… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow/CAF_7M.

<iframe src="https://silytics.si/embed/item/d5240ec5-d88b-4f05-879f-95abe8b71d82" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.