Hybrid prediction

Building predictive models that are a hybrid of data driven statistical machine learning and domain knowledge.

Through Hybrid Prediction, we reimagined existing approaches with new ones to create new opportunities and AI technologies for decision-making in real-time environments.

Hybrid Prediction focused on including physical or theoretical constraints such as conservation of momentum, mass and energy into data driven models.

Hybrid Prediction resulted in replacing components of biophysical models with data driven counterparts, using ML to make them run faster.

Hybrid Prediction  was about incorporating everything we know or observe about a problem to generate predictions that are more efficient and more accurate.  We used  hybrid solutions from Statistics and ML to achieve this.