A New Approach to Forecasting (Paperback)
Language: English
Published by Troubador Publishing, Market Harborough, 2025
- Softcover
- New
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
AbeBooks seller since June 22, 2007
Condition: New
£ 27.98
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Paperback. The greatest original work on forecasting ever published. By a master of the post-Kalman era. Professor O'Reilly brings a lifetimes engineering experience, and not a little scholarship, to an enduring problem. The result: a completely new theory of filtering and prediction for causal dynamical system models subject to significant disturbance uncertainty. Any causal dynamical system model can be used. No a priori knowledge of the model uncertainties is required. Estimation of uncertain dynamical systems, it turns out, is a modelling problem. With necessary model validation. The criterion for high-fidelity signal reconstruction is how closely the signal estimates resemble the measured output data of the actual dynamical system.In contradistinction to the Kalman off-line nominal design approach, the causal estimation approach is an on-line model tuning approach. This physical approach places estimation of dynamical systems on an experimental footing, akin to classical physics and engineering. And closer to present day industrial practice. Both causal and Kalman approaches are evaluated within twentieth century filtering and prediction theory. The new estimator is completely general, non-statistical, and very easy to use. Professor O'Reilly brings a lifetimes engineering experience, and not a little scholarship, to an enduring problem. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…
Seller Inventory # 9781836282860
- Title
- A New Approach to Forecasting (Paperback)
- Author
- John OReilly
- Publisher
- Troubador Publishing, Market Harborough
- Publication year
- 2025
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1836282869
- ISBN 13
- 9781836282860
The greatest original work on forecasting ever published. By a master of the post-Kalman era. Professor O'Reilly brings a lifetime’s engineering experience, and not a little scholarship, to an enduring problem. The result: a completely new theory of filtering and prediction for causal dynamical system models subject to significant disturbance uncertainty. Any causal dynamical system model can be used.
No a priori knowledge of the model uncertainties is required. Estimation of uncertain dynamical systems, it turns out, is a modelling problem. With necessary model validation. The criterion for high-fidelity signal reconstruction is how closely the signal estimates resemble the measured output data of the actual dynamical system.
In contradistinction to the Kalman off-line nominal design approach, the causal estimation approach is an on-line model tuning approach. This physical approach places estimation of dynamical systems on an experimental footing, akin to classical physics and engineering. And closer to present day industrial practice. Both causal and Kalman approaches are evaluated within twentieth century filtering and prediction theory. The new estimator is completely general, non-statistical, and very easy to use.
"Synopsis" may belong to another edition of this title.
About the Author
Professor John O'Reilly is Emeritus Professor of Engineering at the University of Glasgow, UK. One of the greatest engineers of his generation, Professor O'Reilly achieved international standing in several areas of estimation and control before moving to power systems and power electronics. His previous work includes New Directions in Dynamical Systems, Automatic Control and Singular Perturbations.
"About the title" may belong to another edition of this title.
AussieBookSeller
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