Autoregressive Moving - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Autoregressive Moving

The Experts below are selected from a list of 219 Experts worldwide ranked by ideXlab platform

Wang Hong-jun – 1st expert on this subject based on the ideXlab platform

  • Mixture Autoregressive Moving Average Model
    Systems Engineering – Theory & Practice, 2020
    Co-Authors: Wang Hong-jun

    Abstract:

    A new mixture Autoregressive Moving average(MARMA) model is proposed for modeling nonlinear time series.The shape changing feature of conditional distributions makes the MARMA model capable of modeling time series with asymmetric,multimodal distribution,and conditional heteroscedasticity,and so on.The stationary conditions and autocorrelation function of the MARMA process are investigated. The Bayes information criterion(BIC) is used to select the MARMA model.The estimation of parameters is easily performed via expectation maximization(EM) algorithm.The model is applied to a financial data set and compared with other competing models.The MARMA model appears to capture features of the data better than other competing models do.

  • Stationarity conditions for mixture periodic Autoregressive Moving-average time series
    Journal of Shaanxi University of Technology, 2020
    Co-Authors: Wang Hong-jun

    Abstract:

    A new model,MPARMA,which is extended by periodic Autoregressive Moving-average model with the finite mixture modeling,is proposed for modeling the skeness and multimodal in periodic time series.The first moment and the second moment stationarity conditions for MPARMA(p,q) time series are derived.

Dankit K. Nassiuma – 2nd expert on this subject based on the ideXlab platform

  • Gamma and Exponential Autoregressive Moving Average (ARMA) Processes
    Journal of Agricultural Science and Technology, 2005
    Co-Authors: Bs Otieno, Dankit K. Nassiuma

    Abstract:

    Time series data encountered in practice depict properties that deviate from those of gaussian processes. The gamma and exponentially distributed processes which are used as basic models for positive time series fall in the class of non-gaussian processes. In this paper, we develop new and simpler representations of the pth order Autoregressive and the qth order Moving average processes in gamma and exponential variables. The gamma Autoregressive Moving average (GARMA(p,q)) model of order p and q and the exponential Autoregressive Moving average (EARMA(p,q)) model of order p and q are consequently developed. The distributions of developed models, unlike those studied by Lawrance and Lewis (1980), can be determined given either the distribution of the innovation sequence {et} or that of the process itself. The autocorrelation structure, which is a major identification tool in time series, is discussed for each of the proposed models. Journal of Agriculture, Science and Technology Vol.3(1) 2001: 71-80

  • NON-STATIONARY Autoregressive Moving-AVERAGE PROCESSES WITH INFINITE VARIANCE
    Journal of Time Series Analysis, 1993
    Co-Authors: Dankit K. Nassiuma

    Abstract:

    Abstract. In this paper we discuss some properties of non‐stationary Autoregressive Moving‐average processes with Λ‐stationary (0

Paul L. Anderson – 3rd expert on this subject based on the ideXlab platform

  • forecasting with prediction intervals for periodic Autoregressive Moving average models
    Journal of Time Series Analysis, 2013
    Co-Authors: Paul L. Anderson, Mark M Meerschaert, Kai Zhang

    Abstract:

    Periodic Autoregressive Moving average (PARMA) models are indicated for time series whose mean, variance and covariance function vary with the season. In this study, we develop and implement forecasting procedures for PARMA models. Forecasts are developed using the innovations algorithm, along with an idea of Ansley. A formula for the asymptotic error variance is provided, so that Gaussian prediction intervals can be computed. Finally, an application to monthly river flow forecasting is given, to illustrate the method.

  • ASYMPTOTIC RESULTS FOR PERIODIC Autoregressive Moving‐AVERAGE PROCESSES
    Journal of Time Series Analysis, 1993
    Co-Authors: Paul L. Anderson, Aldo V. Vecchia

    Abstract:

    . This paper is concerned with the derivation of asymptotic distributions for the sample autocovariance and sample autocorrelation functions of periodic Autoregressive Moving-average processes, which are useful in modelling periodically stationary time series. In an effort to obtain a parsimonious model representing a periodically stationary time series, the asymptotic properties of the discrete Fourier transform of the estimated periodic autocovariance and autocorrelation functions are presented. Application of the asymptotic results to some specific models indicates their usefulness for model identification analysis.