Predicting Processes When Embedded Events Occur: Dynamic Time Warping

Abstract

In a traditional time-series analysis one assumes a model for the system. This model has a specified form that is consistent over time. However, in most continuous processes changing tank levels, system upsets (events), and transient process behaviors impact the accuracy of forecasts based on these global models. System behavior is often more closely related to local (short-term) process conditions and events that are repeated at random over time. The global model can predict average behavior, but it often does not accurately predict the local behavior of such a system. The traditional purpose of dynamic time warping was to detect predetermined patterns in speech recognition (time-series data). The technique depends on the development of templates, which represent known patterns or behavior. In this paper we extend the dynamic time warping technique to forecast process measurements using recent process data to define a (dynamic) template. This new dynamic template, dynamic-time-warping approach is compared to traditional methods of predicting process measurements when behavior typical of a continuous process is present. The key properties of such processes are described. The dynamic time warping approach is shown to be an effective method of prediction under these process assumptions.