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

Fuzzy Rule Based Image Reconstruction for PET

Emission tomography imaging modality has given a new dimension to the field of medicine and biology. The maximum a posteriori (MAP) and maximum likelihood (ML) algorithms are the widely used reconstruction algorithms for emission tomography. However, the images reconstructed by MAP and ML methods still suffer from artifacts such as noise, over-smoothing and streaking artifacts. These algorithms often fail to recognize the density class in the reconstruction and hence result in over penalization causing blurring effect. A good knowledge of prior distribution is a must for MAP-based method. Recently proposed median root prior (MRP) algorithm preserves the edges in the image, but the reconstructed image suffers from step like streaking artifact. In this work, a fuzzy logic based approach is proposed for the pixel-pixel nearest neighborhood interaction. The proposed algorithm consists of two elementary steps: (1) edge detection-fuzzy rule based derivatives are used for the detection of edges in the nearest neighborhood window; (2) fuzzy smoothing penalization is performed only for those pixels for which edges are missing in the neighborhood window. Analysis shows that the proposed fuzzy rule based reconstruction algorithm is capable of producing better estimates compared to the images reconstructed by MAP and MRP algorithms. The reconstructed images are sharper with small features being better resolved due to the nature of the fuzzy potential function

Fuzzy Rule Based Image Reconstruction for PET

Emission tomography imaging modality has given a new dimension to the field of medicine and biology. The maximum a posteriori (MAP) and maximum likelihood (ML) algorithms are the widely used reconstruction algorithms for emission tomography. However, the images reconstructed by MAP and ML methods still suffer from artifacts such as noise, over-smoothing and streaking artifacts. These algorithms often fail to recognize the density class in the reconstruction and hence result in over penalization causing blurring effect. A good knowledge of prior distribution is a must for MAP-based method. Recently proposed median root prior (MRP) algorithm preserves the edges in the image, but the reconstructed image suffers from step like streaking artifact. In this work, a fuzzy logic based approach is proposed for the pixel-pixel nearest neighborhood interaction. The proposed algorithm consists of two elementary steps: (1) edge detection-fuzzy rule based derivatives are used for the detection of edges in the nearest neighborhood window; (2) fuzzy smoothing penalization is performed only for those pixels for which edges are missing in the neighborhood window. Analysis shows that the proposed fuzzy rule based reconstruction algorithm is capable of producing better estimates compared to the images reconstructed by MAP and MRP algorithms. The reconstructed images are sharper with small features being better resolved due to the nature of the fuzzy potential function

2004 IEEE International Conference on Systems, Man and Cybernetics Fuzzy Rule Based Image Reconstruction for PET

Abstract- Emission tomography imaging modality has given a new dimension to thefield of medicine and biology. The maximum a-posteriori (MAP) and maximum likelihood (') algorithms are the widely used reconshuction algorithms for emission tomography. However, the images reconstructed by MAP and Ml methods still suffer from artfacts such as noise, over-smoothing and streaking artfacts. These algorithms often fail to recognize the density class in the reconshvction and hence result in over-penalization causing blurring eflect. A good howledge of prior distribution is a must for MP-based method. Recently proposed median root prior (A4RP) algorithm preserves the edges in the image, bur the reconstructed image suffersfram step like streaking artfact. In this work, a fuzzy logic based approach is proposed for the pixel-pixel nearest neighborhood interaction. The proposed algorithm consists of two elementary steps: (I) Edge detection-fuzzy rule based derivatives are usedfor the detection of edges in the nearest neighborhood window. (2) Fuzzy smoothing-penalization is performed only for those pixels for which edges are missing in the neighborhood window. Analysis shows that the proposed f uq rule based reconsimction algorithm is capable of producing better estimates compared to the images reconshucted by M P and MRP algorithms. The reconstructed images are sharper with small features being better resolved due to the naiure of the fuzzy potential function