Team mission
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.
🔗 Project information
Machine Learning Research Papers/Sprints
COVID-19 County Dashboard
📋 Roadmap overview
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).
🔍 Detailed quarterly roadmap
Mor
Core Repository Focus Areas
Feature | Priority | Effort | Status | Notes |
---|---|---|---|---|
Adding new time series models/loss functions | MEDIUM | MEDIUM | IN PROGRESS | Leverage PyData Global Sprint |
Meta-data incorporation | HIGH | HIGH | IN PROGRESS | |
Model Interpretability | HIGH | MEDIUM | IN PROGRESS | |
Increasing test coverage | HIGH | MEDIUM | IN PROGRESS | Good to get 3rd party review of training loops. |
Documentation and tutorials | MEDIUM | MEDIUM | IN PROGRESS | |
Auto experimentation | LOW | HIGH | NOT STARTED | |
Cloud Provider Integration | MEDIUM | MEDIUM | IN PROGRESS |
Application/Impact Focus Areas
Feature | Priority | Effort | Status | Notes |
---|---|---|---|---|
COVID County Dashboard | HIGH | HIGH | IN PROGRESS | Link to Slideshow |
River flow dashboard | MEDIUM | HIGH | NOT STARTED | |
River flow open-source | MEDIUM | HIGH | IN PROGRESS |
Research Focus Areas
Focus Area | Priority | Effort | Status | Notes |
---|---|---|---|---|
Incorporating meta-data in TS models | ||||
Transfer learning in time series application | ||||