note

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.

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.

(blue star) Project information

(blue star) 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, flash flood prediction…).

(blue star) Detailed quarterly roadmap

More

Core Repository Focus Areas

Feature

Priority

Effort

Status

Notes

Adding new time series models/loss functions

Leverage PyData Global Sprint

Meta-data incorporation

Model Interpretability

Should add methods other than SHAP.

Increasing test coverage

Need more unit tests of models and meta-data.

Documentation and tutorials

Auto experimentation

This would be useful but requires a lot of effort.

Cloud Provider Integration

Multitask learning support

Application/Impact Focus Areas

Feature

Priority

Effort

Status

Notes

COVID County Dashboard

Link to Slideshow

River flow dashboard

River flow open-source

Research Focus Areas

Focus Area

Priority

Effort

Status

Notes

Incorporating meta-data in TS models

Transfer learning in time series application

Focus areas