Articles liés à Iterative algorithms for short-term forecasting

Iterative algorithms for short-term forecasting - Couverture souple

Kondrashova, Nina

 
9783659634581: Iterative algorithms for short-term forecasting
Couverture souple
ISBN 10 : 3659634581 ISBN 13 : 9783659634581
Editeur : LAP LAMBERT Academic Publishing, 2014

Synopsis

The monograph examines the relaxation iterative algorithms for short-term forecasting of real processes. Their main structural features and the convergence are presented. These algorithms can work as with the small sample sizes (ranging from three records) so with the large amount of data (from three dozen up to thousand variables and up to five dozen of thousand records). Since in these algorithms is observed balance between speed and complexity of models which they build, there are suggested ways to increase the accuracy of solutions if volume of observations is small. For example, to accelerate drag selection for patients, there are used different sample divisions, adaptive prognosis and complex forecasting that takes into account low-frequency (trend) and high-frequency component (residue) of the real process. But one step forward forecast of space weather with a lot of records has shown the high accuracy on the examination sample without mentioned accuracy improvement tools. Comparison of oil price forecasts obtained via GMDH against well known methods showed greater accuracy for the first ones. This book is intended for specialists in the field of forecasting complex systems.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

The monograph examines the relaxation iterative algorithms for short-term forecasting of real processes. Their main structural features and the convergence are presented. These algorithms can work as with the small sample sizes (ranging from three records) so with the large amount of data (from three dozen up to thousand variables and up to five dozen of thousand records). Since in these algorithms is observed balance between speed and complexity of models which they build, there are suggested ways to increase the accuracy of solutions if volume of observations is small. For example, to accelerate drag selection for patients, there are used different sample divisions, adaptive prognosis and complex forecasting that takes into account low-frequency (trend) and high-frequency component (residue) of the real process. But one step forward forecast of space weather with a lot of records has shown the high accuracy on the examination sample without mentioned accuracy improvement tools. Comparison of oil price forecasts obtained via GMDH against well known methods showed greater accuracy for the first ones. This book is intended for specialists in the field of forecasting complex systems.

Biographie de l'auteur

Nina Vladimirovna Kondrashova candidate of sciences, senior researcher is an author of about 75 scientific works in following research areas: inductive modelling, adaptive forecasting, optimal control and medical diagnostics.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2014
Langue
anglais
ISBN 10 
3659634581
ISBN 13 
9783659634581
Reliure
Broché
Nombre de pages
116
Coordonnées du fabricant
non disponible
Personne responsable
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