1. A method for analyzing and predicting the behavior of a system, comprising the steps of: continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial; computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable; computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the time-based baseline mean for the selected input variable; computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial.