Self-tuning filters and predictors for two-dimensional systems Part 3: Prediction applications
J.R. Caldas Pinto, Peter E. Wellstead · International Journal of Control · 1985
The paper describes the practical uses of two-dimensional (2-D) fc-step-ahead self-tuning prediction algorithms. Two distinct application areas are considered. The first concerns direct prediction/forecasting, applied to data with a strong periodic (or 'seasonal’) component. The second concerns the prediction of data from spatial scanning sensors or spatial sensor arrays. In both cases, the original data is usually one-dimensional in nature. The contribution of the paper is to show how, by treating the data as if it were two-dimensional in nature, a vast improvement in the quality of predictions is obtained. Moreover, because the 2-D predictors are self-tuning, the algorithms require virtually no user intervention and no prior filtering or pre-processing of the data.