in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

A new innovations state space modeling framework, incorporating Box-Cox transformations, Fourier series with time varying coefficients and ARMA error correction, is introduced for forecasting complex seasonal time series that cannot be handled using existing forecasting models. Such complex time series include time series with multiple seasonal periods, high frequency seasonality, non-integer s...

Meningkatnya jumlah penduduk membuat kebutuhan akan makanan pokok selalu meningkat, terutama beras. Namun di sisi lain, dengan adanya lahan pertanian semakin menyempit akibatnya beralih fungsi sebagai non pertanian. Hal ini mengakibatkan produksi padi saat tidak mampu mengimbangi pangan Indonesia. Permasalahan yang sering dihadapi petani adalah lemahnya posisi tawar karena kurangnya akses pasar...

Modeling and prediction in some systems requires the simultaneous approximation of mappings and their derivatives to a certain finite order. In this paper, universal approximation capabilities of fuzzy systems are extended to this situation, by showing the denseness of some general classes of fuzzy models in appropriate function spaces where distance between functions is defined in terms of the...

Inflow data plays an important role in water and energy resources planning and management. In general, due to the limited availability of historical inflow data, synthetic streamflow time series have been widely used for several applications such as midand long-term hydropower scheduling and the identification of hydrological processes. This paper explores the use of fuzzy inference systems for...

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 new dual factor time-invariant fuzzy time series method that is capable of forecasting stock market Price Index. The proposed approach uses a new fuzzy logic relationship definition. According to the utilized membership degrees used to define the fuzzy relationships, each datum may belong to two distinct intervals rather than only one interval. This assumption, which has n...