Royal Bank of Canada

Ehsan Amjadian

Graham Alexander Watt

A machine learning architecture is proposed that is directed to receive different time-series data sets relating to environmental conditions as well as a target variable for prediction and to transform the time-series data sets for training a plurality of different machine learning models. The trained machine learning models can be utilized to probe various configurations of environmental conditions, and in some embodiments, conduct first and second order co-efficient of variation determinations to generate one or more data values representative of environmental condition sensitivity metrics.

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Toronto, Canada

Ehsan Amjadian