The Kalman filter is a well-known and efficient recursive algorithm that estimates the state of a dynamic system from a series of indirect and noisy observations of this state. Its applications range from signal processing to machine learning, through speech processing or computer vision. The underlying model usually assumes white noises. Extensions to colored autoregressive (AR) noise model ar...

The adequate modeling and estimation of solar radiation plays a vital role in designing energy applications. In fact, unnecessary environmental changes result several problems with the components photovoltaic affects generation network. Various computational algorithms have been developed over past decades to improve efficiency predicting various input characteristics. This research provides fi...

This paper presents a blind identification algorithm for non-minimum phase single-input single-output (SISO) plants using an over-sampling technique with each input symbol lasting for several sampling periods. First, an SISO autoregressive moving average (ARMA) plant is converted into its associated single-input multi-output (SIMO) system by holding the system input and over-sampling the system...

In the theory of stochastic differential equations, it is commonly assumed that the forcing function is a Wiener process. Such a process has an infinite bandwidth in the frequency domain. In practice, however, all stochastic processes have a limited bandwidth. A theory of band-limited linear stochastic processes is described that reflects this reality, and it is shown how the corresponding ARMA...

This article applied GARCH model instead AR or ARMA model to compare with the standard BP and SVM in forecasting of the four international including two Asian stock markets indices.These models were evaluated on five performance metrics or criteria. Our experimental results showed the superiority of SVM and GARCH models, compared to the standard BP in forecasting of the four international stock...

In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state’s change...

For the multisensor multi-channel autoregressive moving average (ARMA) signals with white measurement noises and an ARMA colored measurement noise as a common disturbance noise, a multi-stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multi...

Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-...

In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real-time monetary aggregate M3 (1977-2000), and residential mortgage credit (1975-1998). The forecasting method we use is multistep-ahead non-adaptive f...

Abstract Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for power, this paper proposes a combined model predicting short-term based on autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build ARMA and GRU respectively to predict power. Then optimize model’s weights by quantum particle swarm algorithm (QPSO). Fina...