Customization of forecasting solutions

Abstract

Forecasting solutions including customization of raw data using uncertainty coefficient and using ensemble neural network architecture. The raw data is customized by cleaning and augmenting to obtain a processed dataset that is non-discreet and continuous, and; the ensemble neural network architecture is customized to include plurality of dependent and independent features to obtain ensembled weights from ensemble recurrent neural network (RNN) type architecture, a one versus rest ensemble RNN architecture and a forest of ensemble, to obtain an appropriate forecasting model that includes dynamically adaptive weights from ground truth along with the ensembled weights to create a final weighted output such that the final weighted output/forecast result has more accuracy and reduced false positives. The present invention allows for feedback mechanism from disruptive forecasting results if any from query module to go back to training module that enhances the accuracy of the next result without re-training.

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