Process monitoring based on large-scale combination of time series data

Abstract

Methods and apparatus are provided for process monitoring based on large-scale combinations of time series data. An exemplary method comprises generating a model from time series data for a given target time series; determining whether a first difference between measured values and predicted values based on the model exceeds a predefined threshold indicating a target prediction error; in response to a detected target prediction error, performing evaluations of (i) a neighborhood coherence comprising an average of variables of the model weighted by corresponding coefficients on a predefined neighborhood time window, and/or (ii) a second difference between a given value of at least one variable in the model and an average value of the at least one variable based on a training dataset; providing notifications when first predefined criteria based on the evaluations are satisfied; and updating the model when second predefined criteria based on the evaluations are satisfied.