Grammar-Based Feature Generation for Time-Series Prediction (Record no. 53246)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03211nam a22005055i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-287-411-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200420221302.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150214s2015 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789812874115 |
| -- | 978-981-287-411-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | De Silva, Anthony Mihirana. |
| 245 10 - TITLE STATEMENT | |
| Title | Grammar-Based Feature Generation for Time-Series Prediction |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XI, 99 p. 28 illus. |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Applied Sciences and Technology, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Feature Selection -- Grammatical Evolution -- Grammar Based Feature Generation -- Application of Grammar Framework to Time-series Prediction -- Case Studies -- Conclusion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Leong, Philip H. W. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-981-287-411-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Singapore : |
| -- | Imprint: Springer, |
| -- | 2015. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Pattern recognition. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Economics, Mathematical. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Pattern Recognition. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Quantitative Finance. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-530X |
| 912 ## - | |
| -- | ZDB-2-ENG |
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