This paper presents Multivariate-Factors fuzzy time series model for improving forecasting accuracy. The proposed model is based on fuzzy clustering and it employs eight main procedures to build the multivariate-factors model. The model is evaluated by studying the Egypt Wheat imports as a forecasting problem. Forecasting Egypt wheat imports depend on three factors: population size, wheat area,...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...

Fuzzy time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are compatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exh ibit periodical changes arising from seasonality. These variations are called seaso...

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignore...

Precipitation forecasting due to its random nature in space and time always faced with many problems and this uncertainty reduces the validity of the forecasting model. Nowadays nonlinear networks as intelligent systems to predict such complex phenomena are widely used. One of the methods that have been considered in recent years in the fields of hydrology is use of wavelet transform as a moder...

The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model provides a powerful representation for time series analysis, modeling and prediction due to its strength to accommodate the dynamic, complex and nonlinear nature of real time series applications. This paper focuses on the modeling and prediction of NARMAX-model-based time series using the fuzzy neural network (FNN)...

Fuzzy Time Series (FTS) models are commonly used in time series forecasting, where they do not require any statistical assumptions on data. FTS can handle data sets with a small number of observations or uncertainty. This is general advantage as compared other techniques. However, still have some criticisms, such the optimal lengths intervals and proper weights, which always influence model acc...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...