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1Forecasting, structural time series models, and the Kalman filter

By A. C. Harvey

“Forecasting, structural time series models, and the Kalman filter” Metadata:

Title: ➤ Forecasting, structural time series models, and the Kalman filter
Author: A. C. Harvey
Language: English
Number of Pages: Median: 554
Publisher: Cambridge University Press
Publish Date: 1989 - 1990 - 1996
Publish Location: Cambridge - New York

“Forecasting, structural time series models, and the Kalman filter” Subjects and Themes:

Subjects: ➤ Kalman filtering - Time-series analysis - Kalman filter - academic - economics - econometrics - Economic forecasting - Economics, statistical methods

Edition Identifiers:

The Open Library ID: OL27251399M - OL22130723M - OL2209051M
Online Computer Library Center (OCLC) ID: 258257421
Library of Congress Control Number (LCCN): 89031417
All ISBNs: 9780521405737 - 0521321964 - 9780521321969 - 0521405734

Access and General Info:

First Year Published: 1989
Is Full Text Available: Yes
Is The Book Public: No
Access Status: Printdisabled

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Kalman filter

In statistics and control theory, Kalman filtering (also known as linear quadratic estimation) is an algorithm that uses a series of measurements observed

Extended Kalman filter

In estimation theory, the extended Kalman filter (EKF) is the nonlinear version of the Kalman filter which linearizes about an estimate of the current

Rudolf E. Kálmán

inventor. He is most noted for his co-invention and development of the Kalman filter, a mathematical algorithm that is widely used in signal processing,

Fast Kalman filter

The fast Kalman filter (FKF), devised by Antti Lange (born 1941), is an extension of the Helmert–Wolf blocking (HWB) method from geodesy to safety-critical

Schmidt–Kalman filter

The Schmidt–Kalman Filter is a modification of the Kalman filter for reducing the dimensionality of the state estimate, while still considering the effects

Ensemble Kalman filter

The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential

Alpha beta filter

data smoothing and control applications. It is closely related to Kalman filters and to linear state observers used in control theory. Its principal

Radar tracker

non-linear filters are: the Extended Kalman filter the Unscented Kalman filter the Particle filter The EKF is an extension of the Kalman filter to cope with

Invariant extended Kalman filter

extended Kalman filter (IEKF) (not to be confused with the iterated extended Kalman filter) was first introduced as a version of the extended Kalman filter (EKF)

Recursive Bayesian estimation

distributed and the transitions are linear, the Bayes filter becomes equal to the Kalman filter. In a simple example, a robot moving throughout a grid