Anthony Mihirana De Silva

(Author)

Grammar-Based Feature Generation for Time-Series Prediction (2015)Paperback - 2015, 17 March 2015

Grammar-Based Feature Generation for Time-Series Prediction (2015)
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Part of Series
Springerbriefs in Applied Sciences and Technology
Part of Series
Springerbriefs in Applied Sciences and Technology / Springer
Part of Series
Springerbriefs in Computational Intelligence
Print Length
99 pages
Language
English
Publisher
Springer
Date Published
17 Mar 2015
ISBN-10
9812874100
ISBN-13
9789812874108

Description

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself. Industrial applications can use the proposed technique to improve their predictions.

Product Details

Authors:
Anthony Mihirana De SilvaPhilip H W Leong
Book Edition:
2015
Book Format:
Paperback
Country of Origin:
NL
Date Published:
17 March 2015
Dimensions:
23.39 x 15.6 x 0.58 cm
Genre:
Science/Technology Aspects
ISBN-10:
9812874100
ISBN-13:
9789812874108
Language:
English
Location:
Singapore
Pages:
99
Publisher:
Weight:
167.83 gm

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