modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

A novel high-order fuzzy time series model for stock price forecasting is presented based on the fuzzy cmeans (FCM) discretization method and artificial neural networks (ANN). In the proposed model, the FCM discretization method obtained reliable interval lengths. In addition, the fuzzy relation matrix was obtained from ANN, mooting the need for complex and time-consuming matrix operations. The...

Research has suggested that outcome feedback is less effective than other forms of feedback in promoting learning by users of decision support systems. However, if circumstances can be identified where the effectiveness of outcome feedback can be improved, this offers considerable advantages, given its lower computational demands, ease of understanding and immediacy. An experiment in stock pric...

The online game industry is an emerging entertainment software industry. It is successful and fast growing. Prior studies have identified characteristics of this industry that are distinctive from traditional entertainment industries. Currently one of the most important business challenges this industry faces is securing investments for development or evaluation of development projects. An onli...

Various classical techniques such as linear regression, nearest neighbor have been used in developing predictive models in the past. But the methodologies developed using fuzzy time series includes a wide array of work that requires special attention. The time series analysis has been of great importance to engineering and economy problems. In this paper, we present a brief summary of the vario...

In this paper, a neural network-driven fuzzy reasoning system for stock price forecast is proposed on the basis of improved Takagi-Sugeno reasoning model. The experimental result shows that the fuzzy neural network has such properties as fast convergence, high precision and strong function approximation ability and is suitable for real stock price prediction.

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine ...

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods...

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (AP ST), which is a two step approach to predict the direction of change of stock price indices. First, performs da...

This research brings forward the Pleione Formosana Hayata Orchid product market demand forecasting system to assist the traditional market personnel to forecast the demand of its customer in the future. The characteristic of Pleione Formosana orchid is one bulb with one leaf only. It sells by the bulbs. By the time for harvesting the operating personnel need spend much capital on the stock of b...