Roadmap
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
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, flash flood prediction…).
Detailed quarterly roadmap
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Core Repository Focus Areas
Feature | Priority | Effort | Status | Notes |
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Adding new time series models/loss functions | Medium | Medium | In progress
| Leverage PyData Global Sprint |
Meta-data incorporation | HIGH | HIGH | In progress |
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Model Interpretability | HIGH | Medium | In progress | Should add methods other than SHAP. |
Increasing test coverage | HIGH | Medium | In progress | Need more unit tests of models and meta-data. |
Documentation and tutorials | Medium | Medium | In progress |
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Auto experimentation | Low | HIGH | not STARTED | This would be useful but requires a lot of effort. |
Cloud Provider Integration | Medium | Medium | In progress |
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Multitask learning support | Medium | HIGH | not STARTED |
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Application/Impact Focus Areas
Feature | Priority | Effort | Status | Notes |
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COVID County Dashboard | HIGH | HIGH | In progress | Link to Slideshow 
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River flow dashboard | mEdium | HIGH | NOT STARTED |
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River flow open-source | mEdium | HIGH | IN PROGRESS |
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Research Focus Areas
Focus Area | Priority | Effort | Status | Notes |
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Incorporating meta-data in TS models |
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Transfer learning in time series application |
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Focus areas