Salesforce/moirai-1.1-R-large

Model

30

stars

5

commits

2

linked in READMEs

Jan 21, 2025

updated

endpoints_compatible
forecasting
foundation models
pretrained models
safetensors
time series
time-series
time-series-forecasting
time series foundation models
transformers
Browse cluster: Time-Series Forecasting & Foundation Models

README

This is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-large). The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository.

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

Contributors

doyensahoo

2 commits

cxiong

1 commits

juncliu

1 commits

twinsken

1 commits

Salesforce/moirai-1.1-R-large

Model

30

stars

5

commits

2

linked in READMEs

Jan 21, 2025

updated

endpoints_compatible
forecasting
foundation models
pretrained models
safetensors
time series
time-series
time-series-forecasting
time series foundation models
transformers
Browse cluster: Time-Series Forecasting & Foundation Models

README

This is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-large). The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository.

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

Contributors

doyensahoo

2 commits

cxiong

1 commits

juncliu

1 commits

twinsken

1 commits