Grammar-Based Feature Generation for Time-Series Prediction (SpringerBriefs in Applied Sciences and Technology / SpringerBriefs in Computational Intelligence)

Anthony Mihirana De Silva

ISBN 10: 9812874100 / ISBN 13: 9789812874108

Published by Springer, 2015

About this Item

2015 edition. 112 pages. 9.00x6.25x0.25 inches. In Stock.

Seller Inventory # x-9812874100

Bibliographic Details

• Title: Grammar-Based Feature Generation for Time-Series Prediction (SpringerBriefs in Applied Sciences and Technology / SpringerBriefs in Computational Intelligence)
• Publisher: Springer
• Publication Date: 2015
• Language: English
• ISBN 10: 9812874100
• ISBN 13: 9789812874108
• Binding: Paperback
• Condition: Brand New
• Weight: 0.23 kilograms

About this title

Synopsis

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.