Classification of generic system dynamics model outputs via supervised time series pattern discovery

Abstract

System dynamics (SD) is a simulation-based approach for analyzing feedback-rich systems. An ideal SDmodeling cycle requires evaluating the qualitative pattern characteristics of a large set of time series model output fortesting, validation, scenario analysis, and policy analysis purposes. This traditionally requires expert judgement, whichlimits the extent of experimentation due to time constraints. Although time series recognition approaches can helpto automate such an evaluation, utilization of them has been limited to a hidden Markov model classifier, namely theIndirect Structure Testing Software (ISTS) algorithm. Despite being used within several automated model-analysis tools,ISTS has several shortcomings. In that respect, we propose an interpretable time series classification algorithm for theSD field, which also addresses the shortcomings of ISTS. Our approach, which can highlight the regions of a certaintime series that are influential in the class assignment, is an extension of the symbolic multivariate time series approachwith the use of a local importance measure. We compare the performance of the proposed approach against both ISTSand nearest-neighbor (NN) classifiers. Our experiments on a SD-specific application show that the proposed approachoutperforms ISTS as well as conventional NN classifiers on both noisy and nonnoisy datasets. Additionally, its classassignments are interpretable as opposed to the other approaches considered in the experiments.