Develop a domain agnostic deep learning for time series forecasting, classification, and anomaly detection framework and leverage it solve pressing problems in health, climate, agriculture, and other high impact humanitarian areas.
Develop a domain agnostic deep learning for time series forecasting, classification, and anomaly detection framework and leverage it solve pressing problems in health, climate, agriculture, and other high impact humanitarian areas.
Machine Learning Research Papers/Sprints
COVID-19 County Dashboard
As explained above we have two major focuses: (1) general repository enhancements that can be applied to any time series problem and (2) vertical impact focus areas that can aim to tackle a specific issue (e.g. COVID spread and flash flood prediction).
Mor
Feature | Priority | Effort | Status | Notes |
---|---|---|---|---|
Adding new time series models/loss functions |
| Leverage PyData Global Sprint | ||
Meta-data incorporation | ||||
Model Interpretability | ||||
Increasing test coverage |
| Good to get 3rd party review of training loops. | ||
Documentation and tutorials | ||||
Auto experimentation | ||||
Cloud Provider Integration |
Feature | Priority | Effort | Status | Notes |
---|---|---|---|---|
COVID County Dashboard | Link to Slideshow | |||
River flow dashboard | ||||
River flow open-source |
Focus Area | Priority | Effort | Status | Notes |
---|---|---|---|---|
Incorporating meta-data in TS models | ||||
Transfer learning in time series application | ||||