Abstract

Being able to reliably forecast future events is important for decision-making and quality improvement projects. A forecaster will rely on historical information of process data, which is analyzed to determine a pattern that can be used to predict future system responses. Many of the current methods of forecasting assume a single global model when forecasting future behavior. Other methods rely on a global concept that allow for parameter adjustments based on current information. The underlying assumptions for these methods, however, do not always reflect the true system behavior. A new approach is proposed that extends the dynamic time warping technique to forecast process measurements using recent process data to define a dynamic template. This approach is compared to traditional methods of predicting process measurements when behavior typical of a continuous process is present.