Model card
Chronos-T5 Base is a specialized time-series forecasting model that treats numerical sequences as language. By leveraging a T5-based encoder-decoder architecture, it reframes forecasting as a text-to-text problem, allowing it to perform zero-shot predictions on unseen datasets without requiring traditional retraining. For developers, this means a significant reduction in the cold-start problem for time-series analysis. It is particularly effective for forecasting trends across diverse domains where historical data is sparse or inconsistent. Integration is straightforward for those familiar with the Hugging Face ecosystem, offering a scalable alternative to traditional statistical models like ARIMA or Prophet by applying transformer-based attention to temporal patterns.
Model files and versions
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amazon/chronos-t5-baseInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model amazon/chronos-t5-baseREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model amazon/chronos-t5-base README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('amazon/chronos-t5-base')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/amazon/chronos-t5-base.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/amazon/chronos-t5-base.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
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