Fr. 72.00

Efficient Mining Of Emerging Patterns In Time Series Stock Data - A Prediction Model

English, German · Paperback / Softback

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Description

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Data mining involves the use of different data analysis tools to find unknown patterns and relationships in large data sets. These tools could include statistical and mathematical models, and machine learning tools. The trading in a stock market involves understanding of various techniques and relevant methodology. Trend analysis and prediction played a vital role in practical stock trading. The data mining researchers have put in great efforts to generate rules and the emphasis was always to generate an optimal number of rules so that an emerging pattern could be identified. It explores the modeling of data mining techniques along with the technical analysis approach. The broad objectives of the book is to provide a framework of a model on the basis of data mining concepts and chart analysis feature of evaluation of the movement of stock market. These two techniques i.e. data mining and chart analysis are explored to have efficiency, accuracy and reliability to determine the parameters for developing a model. Performance metrics were used to ascertain the accuracy level and an error level. A model was proposed and a comparison with existing systems was carried out.

About the author










Il Dr. Mukesh Kumar ha conseguito il BDS e il MDS presso il dipartimento di OMR del JDC. Ha presentato diversi lavori, brevetti e pubblicazioni. Lavora come professore assistente presso il Vyas Dental college and Hospital.Il Dr. Bibin Jacob Emmanuel ha conseguito il BDS presso il Rajas Dental College e il MDS in pedodonzia presso il JDC. Ha molte pubblicazioni e brevetti al suo attivo.

Product details

Authors Arvind Kalia, Mukes Kumar, Mukesh Kumar
Publisher LAP Lambert Academic Publishing
 
Languages English, German
Product format Paperback / Softback
Released 14.03.2017
 
EAN 9783330039445
ISBN 978-3-33-003944-5
No. of pages 224
Subjects Guides
Natural sciences, medicine, IT, technology > IT, data processing

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