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Forecasting Financial Time Series with Grammar‐Guided Feature Generation
Author(s) -
Silva Anthony Mihirana,
Davis Richard I. A.,
Pasha Syed A.,
Leong Philip H. W.
Publication year - 2017
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12083
Subject(s) - computer science , artificial intelligence , machine learning , feature (linguistics) , representation (politics) , grammar , rule based machine translation , data mining , linguistics , philosophy , politics , political science , law
The application of machine learning techniques to forecast financial time series is not a recent development, yet it continues to attract considerable attention because of the difficulty of the problem that is compounded by the nonlinear and nonstationary nature of the time series. The choice of an appropriate set of features is crucial to improve forecasting accuracy of machine learning techniques. In this article, we propose a systematic way for generating rich features using context‐free grammars. Our proposed methodology identifies potential candidates for new technical indicators that consistently improve forecasts compared with some well‐known indicators. The notion of grammar families as a compact representation to generate a rich class of features is exploited, and implementation issues are discussed in detail. The proposed methodology is tested on closing price data of major stock market indices, and the forecasting performance is compared with some standard techniques. A comparison with the conventional approach using standard technical indicators and naive approaches is shown.