introduction: studying the hydrological cycle, especially in large scales such as water catchments, is difficult and complicated despite the fact that the numbers of hydrological components are limited. this complexity rises from complex interactions between hydrological components and environment. recognition, determination and modeling of all interactive processes are needed to address this i...

The well-known prediction-error-based maximum likelihood (PEML) method can only handle minimum phase ARMA models. This likelihood (BFML) method, which can handle nonminimum phase and noncausal ARMA models. The BFML method is identical to the PEML method in the case of a minimum phase ARMA model, and it turns out that the BFML method incorporates a noncausal ARMA filter with poles outside the un...

Often exploration seismic data lacks low and high frequency band signals. The low frequency information provides crucial information about the mean model. Thus, estimation of absolute models using inversion schemes is difficult in case of band limited seismic data. We present a new method to synthesize initial model for inversion of seismic data using autoregressive and moving average modeling....

The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, A...

With the increase of wind power as a renewable energy source in many countries, wind speed forecasting has become more and more important to the planning of wind speed plants, the scheduling of dispatchable generation and tariffs in the day-ahead electricity market, and the operation of power systems. However, the uncertainty of wind speed makes troubles in them. For this reason, a wind speed f...

The purpose of this paper is to introduce a method of estimating parameters in nonnegative ARMA processes. The method is a generalization of the procedures which were derived for autoregressive and moving-average processes. The estimates are constructed in the form of minima of certain fractions or some functions of these minima. A theorem concerning the strong consistence of these estimates is...

Recently, there has been much research on developing models suitable for analysing the volatility of a discrete-time process. Since the volatility process, like many others, is necessarily non-negative, there is a need to construct models for stationary processes which are non-negative with probability one. Such models can be obtained by driving autoregressive moving average (ARMA) processes wi...