Abstract: Methods and systems for process speed-based forecasting, in which historical data of a process is first aligned to account for latency in the process. Optionally, incorporation of memory and/or clustering can be included in the pre-processing of the historical data before being used to train a machine learning model.

Abstract: Disclosed is a computer-implemented method of generating a time-series of data sets for predictive analysis from data comprising input variables and an output variable recorded at sequential time points, the method comprising: dividing the data into a collection of observations, each observation comprising: a subset of sequential time points; associated input variables; and an output variable recorded at a forecasting time point beyond the latest sequential time point of the subset; and using the collection of observations in a convolution neural network to predict the output at the forecasting time point of a streaming data set.

Abstract: Methods and systems for training a neural network in tandem with a policy gradient that incorporates domain knowledge with historical data. Process constraints are incorporated into training through an action mask. Evaluation of the trained network is provided by comparing the network's recommended actions with those of an operator. A decision tree is provided to explain a path from an input of process states, into the neural network, to the output of recommended actions.

Abstract: Methods and systems for process speed-based forecasting, in which historical data of a process is first aligned to account for latency in the process. Optionally, incorporation of memory and/or clustering can be included in the pre-processing of the historical data before being used to train a machine learning model.

Abstract: Disclosed is a computer-implemented method of generating a time-series of data sets for predictive analysis from data comprising input variables and an output variable recorded at sequential time points, the method comprising: dividing the data into a collection of observations, each observation comprising: a subset of sequential time points; associated input variables; and an output variable recorded at a forecasting time point beyond the latest sequential time point of the subset; and using the collection of observations in a convolution neural network to predict the output at the forecasting time point of a streaming data set.