Grammar Based Feature Generation for Time Series Prediction is popular PDF and ePub book, written by Anthony Mihirana De Silva in 2015-02-14, it is a fantastic choice for those who relish reading online the Technology & Engineering genre. Let's immerse ourselves in this engaging Technology & Engineering book by exploring the summary and details provided below. Remember, Grammar Based Feature Generation for Time Series Prediction can be Read Online from any device for your convenience.
Grammar Based Feature Generation for Time Series Prediction Book PDF Summary
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.
Detail Book of Grammar Based Feature Generation for Time Series Prediction PDF
- Author : Anthony Mihirana De Silva
- Release : 14 February 2015
- Publisher : Springer
- ISBN : 9789812874115
- Genre : Technology & Engineering
- Total Page : 99 pages
- Language : English
- PDF File Size : 14,9 Mb
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