Predictive Data Mining Models.

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book&#...

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Bibliographic Details
Main Author: Olson, David L.
Other Authors: Wu, Desheng.
Format: eBook
Language: English
Published: Singapore : Springer Singapore, 2016.
Series: Computational risk management.
Subjects:
Risk management.
Big data.
elektronické knihy
electronic books
ISBN: 9789811025433
9811025428
Physical Description: 1 online resource (105 pages)

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Table of contents

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100 1 |a Olson, David L. 
245 1 0 |a Predictive Data Mining Models. 
260 |a Singapore :  |b Springer Singapore,  |c 2016. 
300 |a 1 online resource (105 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
490 1 |a Computational Risk Management 
505 0 |a Preface; Book Concept; Acknowledgment; Contents; About the Authors; 1 Knowledge Management; 1.1 Computer Support Systems; 1.2 Examples of Knowledge Management; 1.3 Data Mining Forecasting Applications; 1.4 Summary; References; 2 Data Sets; 2.1 Gold; 2.2 Brent Crude; 2.3 Stock Indices; 2.4 Summary; References; 3 Basic Forecasting Tools; 3.1 Moving Average Models; 3.2 Regression Models; 3.3 Time Series Error Metrics; 3.4 Seasonality; 3.5 Daily Data; 3.6 Change in Daily Price; 3.7 Software Demonstrations; 3.8 Summary; 4 Multiple Regression; 4.1 Data Series; 4.2 Correlation; 4.3 Lags; 4.4 Summary. 
505 8 |a 5 Regression Tree Models5.1 R Regression Trees; 5.2 WEKA Regression Trees; 5.2.1 M5P Modeling; 5.2.2 REP Tree Modeling; 5.3 Random Forests; 5.4 Summary; Reference; 6 Autoregressive Models; 6.1 ARIMA Models; 6.1.1 ARIMA Model of Brent Crude; 6.1.2 ARMA; 6.2 GARCH Models; 6.2.1 ARCH(q); 6.2.2 GARCH(p, q) ; 6.2.3 EGARCH; 6.2.4 GJR(p, q); 6.3 Regime Switching Models; 6.3.1 Data; 6.4 Summary; References; 7 Classification Tools; 7.1 Bankruptcy Data Set; 7.2 Logistic Regression; 7.3 Support Vector Machines; 7.4 Neural Networks; 7.5 Decision Trees; 7.6 Random Forests; 7.7 Boosting; 7.8 Full Data. 
505 8 |a 7.9 ComparisonReference; 8 Predictive Models and Big Data; References; Author Index; Subject Index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book's main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Risk management. 
650 0 |a Big data. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Wu, Desheng. 
776 0 8 |i Print version:  |a Olson, David L.  |t Predictive Data Mining Models.  |d Singapore : Springer Singapore, ©2016  |z 9789811025426 
830 0 |a Computational risk management. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-981-10-2543-3  |y Plný text 
992 |c NTK-SpringerBM 
999 |c 97846  |d 97846 

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