An intelligent digital signal processing system for stock trends based upon transient wave detection by using gabor representation and knowledge representation of waveforms

Abstract

AbstractThis paper considers the realization of an intelligent digital signal processing system for representing time series and its applicatio to the classification of stock prices. the system utilizes an effective mechanism to detect the nonstationary part (transient waves) which includes important information on the time series based on the Gabor representation and the knowledge representatio of waveforms using an expert system. In the subsystem for digital signal processing, an adaptive ARMA model is fitted to the time series obtained by subtracting the moving average from the original time series to generate the time series containing enhanced transient waves.The Gabor representation is applid to classify the kinds of transient waves. A set of codebooks for the spectrum of transient waves is used to identify the kind of transient wave. In the subsystem of the expert system, the characteristics of the time series obtained by digital signal processing and other features recognized by conventional methods such as trend lines are transferred to the frames so that the recognition rules of experts are applied to classify the time series. As an application, 194 time series of stock prices are recognized and classified into 11 categories.The result shows that about 80 percent of the times series is recognized, and categories which are close to the original ones are included as the candidates.