Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase

The economic forecasting environment is currently undergoing drastic changes and has a complex and challenging task.Practically, people design a database application or use a statistical package to conduct the analysis on the data.Former approach can be done on the online data, but it must be developed after stating the goal of analysis, which means it only possible for a limited and specific purpose.Whereas the statistical approach must be done for the offline data, however it can lead to the missing pattern and undiscovered knowledge from the available data (Shan, C., 1998).For the effort to extract implicit, previously unknown, hidden and potentially useful information from raw data in an automatic fashion, leads us to the usage of data mining technique that receives big attention from the researchers recently.This paper proposed the issues of joint clustering and knowledge-based neural networks techniques as the application for point forecast decision making.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely. We proposed KMeans clustering algorithm that is based on multidimensional scaling, joined with neural knowledge based technique algorithm for supporting the learning module to generate interesting clusters that will generate interesting rules for extracting knowledge from stock exchange databases efficiently and accurately

An initial state of design and development of intelligent knowledge discovery system for stock exchange database

Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules

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