Add to ORKGThis 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. © 1979 Operational Research Society Ltd
41 pages, 9 figures, correction of errors in the general multivariate caseThe Kalman filter combines...
This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor...
In this book, the authors provide insights into the basics of adaptive filtering, which are particul...
This paper shows that the adaptive filtering and forecasting techniques proposed by Makridakis and W...
In this note, a class of nonlinear dynamic models under rational expectations is studied. A particul...
The Kalman filter is useful to estimate dynamic models via maximum likelihood. To do this the model ...
Adaptive filtering can be used to characterize unknown systems in time-variant environments. The mai...
Typescript (photocopy).This work addresses a long-standing need to create a mathematically credible ...
Includes bibliographical references (page 59)Kalman filters are used to obtain an estimate of a sign...
The Kalman filter (KF) is used extensively for state estimation. Among its requirements are the proc...
These MATLAB files accompany the following publication: Kulikova M.V., Tsyganova J.V. (2015) "Cons...
In this paper, the adaptive filtering theory, recently proposed and developed the authors of present...
Dynamic factor models have become very popular for analyzing high-dimensional time series, and are n...
This paper develops a method of adaptive modeling that may be applied to forecast non-stationary tim...
41 pages, 9 figures, correction of errors in the general multivariate caseThe Kalman filter combines...
This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor...
In this book, the authors provide insights into the basics of adaptive filtering, which are particul...
This paper shows that the adaptive filtering and forecasting techniques proposed by Makridakis and W...
In this note, a class of nonlinear dynamic models under rational expectations is studied. A particul...
The Kalman filter is useful to estimate dynamic models via maximum likelihood. To do this the model ...
Adaptive filtering can be used to characterize unknown systems in time-variant environments. The mai...
Typescript (photocopy).This work addresses a long-standing need to create a mathematically credible ...
Includes bibliographical references (page 59)Kalman filters are used to obtain an estimate of a sign...
The Kalman filter (KF) is used extensively for state estimation. Among its requirements are the proc...
These MATLAB files accompany the following publication: Kulikova M.V., Tsyganova J.V. (2015) "Cons...
In this paper, the adaptive filtering theory, recently proposed and developed the authors of present...
Dynamic factor models have become very popular for analyzing high-dimensional time series, and are n...
This paper develops a method of adaptive modeling that may be applied to forecast non-stationary tim...
41 pages, 9 figures, correction of errors in the general multivariate caseThe Kalman filter combines...
This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor...
In this book, the authors provide insights into the basics of adaptive filtering, which are particul...