Abstract: Presented herein are methods and systems for generating and executing applications that provide insights to a model's operation without requiring the user to have knowledge of coding, computer programming, or artificial intelligence machine-learning methodologies. An exemplary method includes deploying a model using input data to generate a predicted dataset; presenting indications for a plurality of applications associated with the deployed model including an configured to generate new scenarios and another application configured to optimize at least one feature; presenting a plurality of features analyzed by the model; and in response to receiving a selection of a feature of the plurality of features and a new value for the feature, executing the first application to generate a second predicted dataset using the new value.

Abstract: This disclosure relates generally to using machine learning models to generate current time-series features using machine learning and validate time-series machine learning model output. At least one aspect is directed to a system with one or more processors, coupled to memory, to segment a time series range into a first segment for an instance of time, the segment associated with a value for a target feature and a timestamp for the value, segment the time series range into an input segment associated with a plurality of input features and a segment timestamp less than or equal to the timestamp, generate a model trained with input comprising values for the target feature and timestamps for the values less than or equal to the segment timestamp, and transform at least one of the input features based at least on the model.