We consider two relations between Fisher's information matrix of a stationary ARMA (autoregressive moving average) process and Sylvester's resultant matrix. One is based on the Wald test statistic for testing common roots of the AR and MA polynomials of an ARMA process, and the other one is established by using the structure of Fisher's information matrix. It turns out that the latter is also a...

In this note we consider the autoregressive moving average recurrent neural network ARMA-NN(1; 1) process. We show that in contrast to the pure autoregressive process simple ARMA-NN processes exist which are not irreducible. We prove that the controllability of the linear part of the process is sufficient for irreducibility. For the irreducible process essentially the shortcut weight correspond...

The queries entered into search engines register hundreds of millions of different searches by tourists, not only reflecting the trends of the searchers' preferences for travel products, but also offering a prediction of their future travel behavior. This study used web search query volume to predict visitor numbers for a popular tourist destination in China, and compared the predictive power o...

In this paper, the short-term load forecast by use of autoregressive moving average (ARMA) model including non-Gaussian process considerations is proposed. In the proposed method, the concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process. With embodiment of a Gaussianity verification procedure, the forecasted model is identi...

Predicting stock price is a trend yet very challenging task. It because the prices depend upon several internal and external factors. Stock prediction can be useful for financial sectors government help in informed decision-making. This paper analyzes market of K-Electric Karachi. found that K-electric on refinery sector. The data two sectors. Also, compares based moving average, auto-regressiv...

Horizontal displacement of Hoa Binh dam in operation phase is analyzed and then forecasted by using three methods: the multi-regression model (MTR), Seasonal Integrated Auto-regressive Moving Average (SARIMA) Back-propagation Neural Network (BPNN) model. The monitoring data Dam 137 periods, including horizontal displacement, time, reservoir water level air temperature, are used for experiments....

In this paper we address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise terms. Using regret minimization techniques, we develop effective online learning algorithms for the prediction problem, without assuming that the noise terms are Gaussian, identically distributed or even independent. Furthermore, we show ...

Air pollution is a worldwide issue that affects the lives of many people in urban areas. It considered air may lead to heart and lung diseases. A careful timely forecast quality could help reduce exposure risk for affected people. In this paper, we use data-driven approach predict based on historical data. We compare three popular methods time series prediction: Exponential Smoothing (ES), Auto...

In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between 'input' and 'output' time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modelin...

The problem of separating two or more uncorrelated signals from equally many observed mixtures is considered in this thesis. The observed signals are modeled as a sum of original signals ltered through linear lters. Various kinds of mixing lters are considered: Finite Impulse Response (FIR) and Auto Regressive Moving Average (ARMA), causal and non-causal, one and two-dimensional. A separation s...