today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear sys...

Overly generalized predictions are a serious problem in concept classi!cation. In particular, the boundaries among classes are not always clearly de!ned. For example, there are usually uncertainties in diagnoses based on data from biochemical laboratory examinations. Such uncertainties make the prediction be more di4cult than noise-free data. To avoid such problems, the idea of fuzzy classi$cat...

The aim of this contribution is to show the opportunities of applying of fuzzy time series models to predict multiple heterogeneous time series, given at International Time Series Forecasting Competition [http://irafm.osu.cz/cif/main.php]. The dataset of this competition includes 91 time series of different length, time frequencies and behaviour. In this paper the framework (algorithm) of multi...

Reliable prediction of sales can improve the quality of business strategy. In this research, fuzzy logic and artificial neural network are integrated into the fuzzy back-propagation network (FBPN) for sales forecasting in Printed Circuit Board (PCB) industry. The fuzzy back propagation network is constructed to incorporate production-control expert judgments in enhancing the model’s performance...

Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not ful...

Several scientific forecasting models for presidential elections have been suggested. However, most of these models are based on traditional statistics approaches. Since the system is linguistic, vague, and dynamic in nature, the traditional rigorous mathematical approaches are inappropriate for the modeling of this kind of humanistic system. This paper presents a combined neural fuzzy approach...

Flood forecasting is always a challenge in Taiwan, which has a subtropical climate and high mountains. This paper develops a fuzzy clustering neural network (FCNN), and implements this novel structure and reasoning process for flood forecasting. The FCNN has a hybrid learning scheme; the unsupervised learning scheme employs fuzzy min-max clustering to extract information from the input data. Th...

Fuzzy time series forecasting model is one of the tools that can be used to identify factors in order to solve the complex process and uncertainty, nowadays widely used in forecasting problems, but having appropriate universe of discourse and interval length are two subjects that exist in the Fuzzy time series. Recently Adaptive Time-Variant Model for fuzzy time series (ATVF) has been proposed ...

Foreign exchange rate (forex) forecasting has been the subject of several rigorous investigations due to its importance in evaluating the bene ̄ts and risks of the international business environments. Many methods have been researched with the ultimate goal being to increase the reliability and e±ciency of the forecasting method. However as the data are inherently dynamic and complex, the develo...

Stock market forecasting research offers many challenges and opportunities, with the forecasting of individual stocks or indexes focusing on forecasting either the level (value) of future market prices, or the direction of market price movement. A three-stage stock market prediction system is introduced in this article. In the first phase, Multiple Regression Analysis is applied to define the e...