This article describes the influence of the kind of membership function on the accuracy of fuzzy logic forecasting model in the local power system. Fuzzy logic approach overcomes some problems related to practical implementations of traditional modelling and forecasting methods. This qualitative method of load forecasting can incorporate imprecise and ambiguous information in reasoning. The com...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

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 ...

Vague and incomplete data represented as linguistic values massively exists in diverse real-word applications. The task of forecasting fuzzy time series under uncertain circumstances is thus of great important but difficult. The inherent uncertainty involving time evolution usually makes the transition of states in a system probabilistic. In this paper, we proposed a new forecasting model based...

This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that th...

Abstract—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 improve forecasting accuracy. Recently, Fuzzy time-series based on Fibonacci sequence has been proposed as a new fuzzy time series model which incorporates the concept of the Fibonacci sequence, the f...

In recent years, some researchers used high-order fuzzy time series to deal with forecasting problems. In this paper, we present a new method for forecasting the enrollments of the University of Alabama based on the high-order fuzzy time series. The proposed method uses the socalled “second order differences” of the enrollments of the previous years to determine the trend of the forecasting. Th...

Fuzzy time series forecasting methods do not require constraints found in conventional approaches. In addition, due to uncertainty that they contain, many time series to be forecasted should be considered as fuzzy time series. Fuzzy time series forecasting models consist of three steps as fuzzification, identification of fuzzy relations and defuzzification. Although most of the time series enco...

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,...

Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time,...