Adaptive Filtering Revisited

This paper shows that the adaptive filtering and forecasting techniques proposed by Makridakis and Wheelwright can be viewed as approximations to a more precise filtering method in which the Kalman filter is applied to a dynamic autoregressive model which is a special case of the models of Harrison and Stevens. The correct "learning" or "training factors" are shown to be data-dependent matrices rather than scalar constants.