Overview
- First book presenting the framework for context-free grammar-based feature generation
- Equips readers to predict time-series prediction using machine learning techniques
- Includes case studies that illustrate the performance of different machine learning and model based approaches on financial, electrical and foreign exchange client trade volume time-series data
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (7 chapters)
Keywords
About this book
Authors and Affiliations
Bibliographic Information
Book Title: Grammar-Based Feature Generation for Time-Series Prediction
Authors: Anthony Mihirana De Silva, Philip H. W. Leong
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-981-287-411-5
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2015
Softcover ISBN: 978-981-287-410-8Published: 17 March 2015
eBook ISBN: 978-981-287-411-5Published: 14 February 2015
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XI, 99
Number of Illustrations: 28 b/w illustrations
Topics: Computational Intelligence, Pattern Recognition, Quantitative Finance