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Filtering and smoothing of financial data using Wavelets: New trends in filtering and smoothing - Couverture souple

Saleemi, Afzal; Raza, Akhter

 
9783847341093: Filtering and smoothing of financial data using Wavelets: New trends in filtering and smoothing
Couverture souple
ISBN 10 : 384734109X ISBN 13 : 9783847341093
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

As temporal data are of growing importance hence require to support new trends for filtering and smoothing. The main goal of this work is to explore new smoothing techniques like wavelet filters in place of traditional moving averages and regression methods, etc for temporal data for further decision making processes like forecasting, similarities search and clustering. Temporal applications are increasingly popular methods for studying a wide range of databases including sensor, weather, stock exchange, seismic and atomic, etc. Time series, a common form of temporal data, are mostly contaminated with noise and outliers. The direct operation and analysis on these types of raw data, may easily lead to wrong decisions. The selection of appropriate smoothing techniques is the pivotal point to reach to the correct conclusions. In this work, the main task has been to select the best possible smoothing technique for selection of features of time series to perform various tasks.

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

Présentation de l'éditeur

As temporal data are of growing importance hence require to support new trends for filtering and smoothing. The main goal of this work is to explore new smoothing techniques like wavelet filters in place of traditional moving averages and regression methods, etc for temporal data for further decision making processes like forecasting, similarities search and clustering. Temporal applications are increasingly popular methods for studying a wide range of databases including sensor, weather, stock exchange, seismic and atomic, etc. Time series, a common form of temporal data, are mostly contaminated with noise and outliers. The direct operation and analysis on these types of raw data, may easily lead to wrong decisions. The selection of appropriate smoothing techniques is the pivotal point to reach to the correct conclusions. In this work, the main task has been to select the best possible smoothing technique for selection of features of time series to perform various tasks.

Biographie de l'auteur

Dr. M. Afzal Saleemi completed his Ph.D. in Computer Science in 2007 from University of Karachi. He is working as head of Computer Science Department National University of Sciences and Technology, NUST (PMA). His areas of interests are Wavelets, Data and Knowledge Engineering, Data mining, Database Management System, and Soft computing.

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
2012
Langue
anglais
ISBN 10 
384734109X
ISBN 13 
9783847341093
Reliure
Broché
Nombre de pages
128
Coordonnées du fabricant
non disponible
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