This paper presents an integration prediction method which is called a hybrid forecasting system based on multiple scales. In this method, the original data are decomposed into multiple layers by the wavelet transform and the multiple layers are divided into low-frequency, intermediate-frequency and high-frequency signal layers. Then autoregressive moving average models, Kalman filters and Back...

This study investigates the algorithm of effective option trading strategy based on the superior volatility forecasts using actual option price data in Taiwan stock market. Forecast evaluation supports the significant incremental explanatory power of investor sentiments on the fitting and forecasting of future volatility to its adversarial multiple-factor model, especially the market turnover a...

This paper describes the prediction scheme of stock price by using multiagent systems. Agents predict the stock price according to their strategies which is defined from technical and fundamental parameters such as some index related to the stock price, the currency exchange rate of the Japanese Yen (JPY) against the US Dollar and so on. Agents are randomly generated to construct population and...

Stock trading plays an important role for supporting profitable stock investment. In particular, more and more data mining-based technical trading rules have been developed and used in stock trading systems to assist investors with their smart trading decisions. However, many mined trading rules are of no interest to traders and brokers because they are discovered based on statistical significa...

Supply chain management; Fuzzy clustering; Interval type-2 fuzzy hybrid system; Demand forecasting; Ordering policy; Bullwhip effect. Abstract The purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments by means of type-2 fuzzymethodology. In order to reduce the bullwhip effect in a supply chain, we propose a newmethod for demand forecasting. First, the demand ...

In this paper we introduce a modification of the real discrete Fourier transform and its inverse transform to filter noise and perform reduction on the data whilst preserving the trend of global moving of time series. The transformed data is still in the same time domain as the original data, and can therefore be directly used by any other mining algorithms. We also present a classification alg...

We applied genetic algorithms to fuzzy rule generation to compute expert system rules from data. We have attempted to improve on existing techniques for the automatic generation of fuzzy logic expert system rules with a method we call genetic data clustering (GDC). A genetic algorithm groups training data points by their degree of similarity, and fuzzy logic expert system rules are formed from ...

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment de...