New linear predictive methods for digital speech processing

Speech processing is needed whenever speech is to be compressed, synthesised or recognised by the means of electrical equipment. Different types of phones, multimedia equipment and interfaces to various electronic devices, all require digital speech processing. As an example, a GSM phone applies speech processing in its RPE-LTP encoder/decoder (ETSI, 1997). In this coder, 20 ms of speech is first analysed in the short-term prediction (STP) part, and second in the long-term prediction (LTP) part. Finally, speech compression is achieved in the RPE encoding part, where only 1/3 of the encoded samples are selected to be transmitted. This thesis presents modifications for one of the most widely applied techniques in digital speech processing, namely linear prediction (LP). During recent decades linear prediction has played an important role in telecommunications and other areas related to speech compression and recognition. In linear prediction sample s(n) is predicted from its p previous samples by forming a linear combination of the p previous samples and by minimising the prediction error. This procedure in the time domain corresponds to modelling the spectral envelope of the speech spectrum in the frequency domain. The accuracy of the spectral envelope to the speech spectrum is strongly dependent on the order of the resulting all-pole filter. This, in turn, is usually related to the number of parameters required to define the model, and hence to be transmitted. Our study presents new predictive methods, which are modified from conventional linear prediction by taking the previous samples for linear combination differently. This algorithmic development aims at new all-pole techniques, which could present speech spectra with fewer parameters.reviewe

Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting

This research presents a study of intelligent stock price forecasting systems using interval type-2 fuzzy logic for analyzing Japanese candlestick techniques. Many intelligent financial forecasting models have been developed to predict stock prices, but many of them do not perform well under unstable market conditions. One reason for poor performance is that stock price forecasting is very complex, and many factors are involved in stock price movement. In this environment, two kinds of information exist, including quantitative data, such as actual stock prices, and qualitative data, such as stock traders\u27 opinions and expertise. Japanese candlestick techniques have been proven to be effective methods for describing the market psychology. This study is motivated by the challenges of implementing Japanese candlestick techniques to computational intelligent systems to forecast stock prices. The quantitative information, Japanese candlestick definitions, is managed by type-2 fuzzy logic systems. The qualitative data sets for the stock market are handled by a hybrid type of dynamic committee machine architecture. Inside this committee machine, generalized regression neural network-based experts handle actual stock prices for monitoring price movements. Neural network architecture is an effective tool for function approximation problems such as forecasting. Few studies have explored integrating intelligent systems and Japanese candlestick methods for stock price forecasting. The proposed model shows promising results. This research, derived from the interval type-2 fuzzy logic system, contributes to the understanding of Japanese candlestick techniques and becomes a potential resource for future financial market forecasting studies --Abstract, page iii

Agents in the market place an exploratory study on using intelligent agents to trade financial instruments

Tese de doutoramento em InformáticaThis dissertation documents our exploratory research aimed at investigating the utilization of intelligent agents in the development of automated financial trading strategies. In order to demonstrate this potential use for agent technology, we propose a hybrid cognitive architecture meant for the creation of autonomous agents capable of trading different types of financial instruments. This architecture was used to implement 10 currency trading agents and 25 stock trading agents. Their overall performance, evaluated according to the cumulative return and the maximum drawdown metrics, was found to be acceptable in a reasonably long simulation period. In order to improve this performance, we defined negotiation protocols that allowed the integration of the 35 trading agents in a multi-agent system, which proved to be better suited for withstanding sudden market events, due to the diversification of the investments. This system obtained very promising results, and remains open to many obvious improvements. Our findings lead us to conclude that there is indeed a place for intelligent agents in the financial industry; in particular, they hold the potential to be employed in the establishment of investment companies where software agents make all the trading decisions, with human intervention being relegated to simple administrative tasks.Esta dissertação documenta um estudo exploratório destinado a investigar a utilização de agentes inteligentes no desenvolvimento de estratégias de investimento financeiro automatizadas. Para demonstrar este uso potencial para tecnologia de agentes, foi proposta uma arquitectura cognitiva híbrida destinada à criação de agentes autónomos capazes de negociar diferentes tipos de instrumentos financeiros. Esta arquitectura foi utilizada para implementar 10 agentes que negoceiam pares cambiais, e 25 agentes que negoceiam acções. A performance global destes agentes, avaliada de acordo com as métricas de retorno acumulado e drawdown máximo, foi considerada aceitável ao longo de um período de simulação relativamente longo. Para melhorar esta performance, foram definidos protocolos de negociação que permitiram a integração dos 35 agentes num sistema multi-agente, que demonstrou estar melhor preparado para enfrentar alterações súbitas nos mercados, devido à diversificação dos investimentos. Este sistema obteve resultados muito promissores, e pode ainda ser sujeito a diversos melhoramentos. Os nossos resultados indiciam que os agentes inteligentes podem ocupar um lugar de relevo na indústria financeira; em particular, aparentam ter potencial suficiente para serem aplicados na criação de fundos de investimento onde todas as decisões de negociação são efectuadas por agentes de software, sendo a intervenção humana relegada para tarefas administrativas básicas

Stock trading and prediction using neural networks

This paper investigates the method of predicting stock price trends using rule-based neural network which was initially proposed by Seng-cho Timothy Chou, Chau-chen Yang, Chi-huang Chen and Feipei Lai in their paper “A Rule-based Neural Stock Trading Decision Support System” [27]. Artificial neural network (ANN) has one input layer, one hidden layer and one output layer for supervised learning and prediction. The neurogenetic model is trained by input features, which are derived from a number of technical indicators being used by financial experts. After this, a new set of test data will be put into the model for prediction. The genetic algorithm (GA) optimizes the NN’s weights in the mean time. The output from the neural network will be used to make trading decision based on the trading rule and threshold value determined. By testing the proposed method with 18 companies in NYSE and NASDAQ for 10 years from 1999 to 2009, an encouraging result has been showed.Bachelor of Engineerin