Abstract: An artificial intelligence system and method for real-time event trend analysis are provided for a population of machine learning models configured to monitor a real-time data stream. A controller is configured for analyzing the population of machine learning models and determining data trends in response to changes in the real-time data stream; receiving a collective output from the population of machine learning models, wherein the output comprises an analysis of the real-time data stream; extracting an event horizon data trend based on the collective output, the event horizon data trend comprising a determined upcoming data variation in the real-time data stream; and continuously reconfiguring the population of machine learning models based on the collective output and the event horizon data trend.

Abstract: An artificial intelligence system and method for real-time event trend analysis are provided for a population of machine learning models configured to monitor a real-time data stream. A controller is configured for analyzing the population of machine learning models and determining data trends in response to changes in the real-time data stream; receiving a collective output from the population of machine learning models, wherein the output comprises an analysis of the real-time data stream; extracting an event horizon data trend based on the collective output, the event horizon data trend comprising a determined upcoming data variation in the real-time data stream; and continuously reconfiguring the population of machine learning models based on the collective output and the event horizon data trend.

Abstract: An artificial intelligence system and method for improving machine learning model adaptability are provided for a population of machine learning models configured to monitor a real-time data stream. A controller is configured for training and reconfiguring the population of the machine learning models in response to changes in the data stream; continuously monitor the population of the machine learning models, wherein continuously monitoring the population comprises collecting performance metrics for each of the machine learning models; analyze the performance metrics for each of the machine learning models by comparing the performance metrics to threshold values; and based on analyzing the performance metrics, reconfigure the population of the machine learning models.