The WorldWide Web is thèkiller application' of the mid-1990's, and as a result it serves as the market place for various services. This position paper discusses the problem of creating complex Web services from simpler ones, under client control. For example, one might want an annotated forecast of a company's stock price from three simple services, viz., stock price data, forecasting, and anno...

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similari...

We conduct evolutionary programming experiments to evolve artificial neural networks for forecast combination. Using stock price volatility forecast data we find evolved networks compare favorably with a naı̈ve average combination, a least squares method, and a Kernel method on out-of-sample forecasting ability—the best evolved network showed strong superiority in statistical tests of encompassi...

This paper proposed a model to predict the stock price based on combining Self-Organizing Map (SOM) and fuzzy – Support Vector Machines (f-SVM). Extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is the base of this proposed approach. In the proposed model, SOM is used as a clustering algorithm to partition the whole input space into several di...

Stock market forecasting is important and interesting, because the successful prediction of stock prices may promise attractive benefits. The economy of Taiwan relies on international trade deeply, and the fluctuations of international stock markets will impact Taiwan stock market. For this reason, it is a practical way to use the fluctuations of other stock markets as forecasting factors for f...

The daily stock turning point detection problems are investigated in this study. The Support Vector Regression model has been applied in various forecasting applications and proved to be with stable performances. In this research, SVR has been used to predict the trading signal since it could handle overall information effectively even under the complex environment of stock price variations. Th...

The main purpose of this paper is to compare the support vector machine (SVM) developed by Vapnik with other techniques such as Backpropagation and Radial Basis Function (RBF) Networks for financial forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results f...