|
|
--- |
|
|
license: cc-by-nc-4.0 |
|
|
pipeline_tag: time-series-forecasting |
|
|
tags: |
|
|
- time-series forecasting |
|
|
- expert selection |
|
|
- LLM |
|
|
--- |
|
|
|
|
|
# MoiraiAgent - Expert-selection Tool |
|
|
|
|
|
Top-performing forecasters exhibit unique advantages when applied to time series from specific domains or with particular patterns. |
|
|
MoiraiAgent can select the best prediction from a set of candidates. Expert-selection by MoiraiAgent boosts the time-series foreacsting performance significantly. |
|
|
|
|
|
|
|
|
## Usage |
|
|
|
|
|
1. Clone repository: |
|
|
```shell |
|
|
git clone https://github.com/SalesforceAIResearch/uni2ts.git |
|
|
cd uni2ts |
|
|
``` |
|
|
|
|
|
2) Create virtual environment: |
|
|
```shell |
|
|
virtualenv venv |
|
|
. venv/bin/activate |
|
|
``` |
|
|
|
|
|
3) Run a demo: |
|
|
```shell |
|
|
python eval.py |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
## Citation |
|
|
|
|
|
```markdown |
|
|
A LINK TO THE BLOG |
|
|
``` |
|
|
|
|
|
## 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. |