In this paper we used the technique of wavelets with fuzzy logic to forecast enrollment of Alabama university from 1971 to 1994 where data were taken and analyzed by using wavelets, then logic , and we used the mean square error (MSE) to compare the forecasting results with previous different forecasting methods. The results were acceptable compared with the results of previous research. 1Intro...

Two important applications of rainfall-runoff models are forecasting and simulation. At present, rainfall-runoff models based on artificial intelligence methods are built basically for short-term forecasting purposes and these models are not very effective for simulation purposes. This study explores the applicability and effectiveness of adaptive neuro-fuzzy-system-based rainfall-runoff models...

In this paper we apply an Adaptive Network-Based Fuzzy Inference System (ANFIS) with one input, the dependent variable with one lag, for the forecasting of four macroeconomic variables of US economy, the Gross Domestic Product, the inflation rate, six monthly treasury bills interest rates and unemployment rate. We compare the forecasting performance of ANFIS with those of the widely used linear...

The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improving forecasting accuracy, however, the issue of partitioning intervals has rarely been investigated. Recently, we proposed a novel deterministic forecasting model to eliminate the major overhead of d...

In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficient ...

Sales demand forecasting is an important issue for manufacturing companies. Indeed, such forecasting is decisive in the management of the production systems. This work compares the performance of an Artificial Neural Network to traditional methods in forecasting the sales demand in a textile factory over a reduced data set.

The architecture of forecasting adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting tasks are proposed. The learning algorithm is optimal on rate of convergence and allows to tune both the synaptic weights and dilations and translations parameters of wavelet activation functions. The simulation of developed wavelet-neuro-fuzzy n...

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...

To further enhance the accuracy and precision of DRAM price forecasting, a hybrid fuzzy and neural approach with virtual experts and partial consensus is proposed. In the proposed methodology, some virtual experts form a committee. These virtual experts construct their own fuzzy linear regression (FLR) equations to forecast the price of a DRAM product from various viewpoints. Each FLR equation ...

The fuzzy time series has recently received increasing attention because of its capability in dealing with vague and incomplete data. This article presents an improved fuzzy time series model; we show that our method is as complete as the original definition but with higher reliability (Chou and Lee). Experimental results using the University of Alabama’s enrollment data (adapted by Song and Ch...