Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for representing and forecasting monthly and ten-day streamflow in three Indian rivers. The best models for forecasting and representation of data are selected by using the criteria of Minimum Mean Square Error (MMSE) and Maximum Likelihood (ML) respectively. The selected models are validated for significa...

In this paper a new methodology is presented for restoring radiological images degraded during acquisition and processing. Details of the work, carried out to optimize a neural network (NN) for identifying an autoregressive moving average (ARMA) model used for nonlinearly degraded image restoration, are presented in this paper. The degraded image is expressed as an ARMA process. To improve the ...

With the rapid development of economy, investment has become a hot word. Many people hope to find an method make profits. methods such as stocks, wealth management and funds have emerged. In process investment, forecasting trend products is one most important links. This paper analyzes time series APPLE, AMERICAN AIRLINES AMD based on ARMA-GARCH model, evaluates model according AIC, BIC, HQIC o...

Predicting both the time and the location of human movements is valuable but challenging for a variety of applications. To address this problem, we propose an approach considering both the periodicity and the sociality of human movements. We first define a new concept, Social Spatial-Temporal Event (SSTE), to represent social interactions among people. For the time prediction, we characterise t...

This paper investigates the impact of dependent but uncorrelated innovations (errors) on the traditional autoregressive moving average model (ARMA) order determination schemes such as autocorrelation function (ACF), partial autocorrelation function (PACF), extended autocorrelation function (EACF) and unit-root test. The ARMA models with iid innovations have been studied extensively and are well...

Considering absolute log returns as a proxy for stochastic volatility, the influence of explanatory variables on absolute log returns of ultra high frequency data is analysed. The irregular time structure and time dependency of the data is captured by utilizing a continuous time ARMA(p,q) process. In particular we propose a mixed effect model for the absolute log returns. Explanatory variable i...

In recent decades, the momentum of global environmental protection has culminated in the Kyoto Agreement of 1998, placing the limelight on “green” issues. This paper argues that the protection of environmental systems involves a fragile balance between the costs of environment preservation and the profit motivations of industrialists. In particular, one of the issues that needs to be addressed ...

This paper presents a method to improve implementation accuracy of a recently proposed CORDIC ARMA lattice filter. Since the CORDIC ARMA lattice filter algorithm has a problem in its shift sequence, i t cannot implement a lattice filter accurately. Therefore, in this paper we apply the shift sequence proposed by Walther without the problem to the CORDIC ARMA lattice filter, and then we realize ...

A class of autoregressive moving-average (ARMA) models proposed by J rgensen and Song [Journal of Applied Probability (1998), vol. 35, pp. 78–92] with exponential dispersion model margins are useful to deal with non-normal stationary time series with high-order autocorrelation. One property associated with the class of models is that the projection process takes the exact form of the classical ...