Autoregressive Moving

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Wang Hong-jun - One of the best experts 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 - One of the best experts 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 - One of the best experts 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.

Zhi Hu - One of the best experts on this subject based on the ideXlab platform.

  • wind speed forecasting using Autoregressive Moving average generalized Autoregressive conditional heteroscedasticity model
    European Transactions on Electrical Power, 2012
    Co-Authors: Wen Jiang, Donghan Feng, Zhi Hu
    Abstract:

    SUMMARY In this paper, a hybrid model of Autoregressive Moving average and generalized Autoregressive conditional heteroscedasticity is proposed to forecast wind speed. In this model, the conditional variance of an observation depends linearly on the conditional variance of the previous observations and on the previous prediction errors. This conditional variance can capture the feature that the predictability of meteorological variables is not constant but shows regular variations. The quasi-maximum likelihood estimator was used to estimate parameters of the proposed model. An improved particle swarm optimization was proposed to solve the solution of the Autoregressive Moving average/generalized Autoregressive conditional heteroscedasticity model through the log-quasi-likelihood function. Four different indices are introduced to demonstrate the performance of the proposed model. Generated results of different season sample sets were compared with their corresponding values when using the Autoregressive Moving average model. The simulation results validate the effectiveness, accuracy, and superiority of the proposed model for wind speed prediction. Copyright © 2011 John Wiley & Sons, Ltd.

  • Wind speed forecasting using Autoregressive Moving average/generalized Autoregressive conditional heteroscedasticity model
    European Transactions on Electrical Power, 2011
    Co-Authors: Wen Jiang, Donghan Feng, Zhi Hu
    Abstract:

    SUMMARY In this paper, a hybrid model of Autoregressive Moving average and generalized Autoregressive conditional heteroscedasticity is proposed to forecast wind speed. In this model, the conditional variance of an observation depends linearly on the conditional variance of the previous observations and on the previous prediction errors. This conditional variance can capture the feature that the predictability of meteorological variables is not constant but shows regular variations. The quasi-maximum likelihood estimator was used to estimate parameters of the proposed model. An improved particle swarm optimization was proposed to solve the solution of the Autoregressive Moving average/generalized Autoregressive conditional heteroscedasticity model through the log-quasi-likelihood function. Four different indices are introduced to demonstrate the performance of the proposed model. Generated results of different season sample sets were compared with their corresponding values when using the Autoregressive Moving average model. The simulation results validate the effectiveness, accuracy, and superiority of the proposed model for wind speed prediction. Copyright © 2011 John Wiley & Sons, Ltd.

Helmut Lütkepohl - One of the best experts on this subject based on the ideXlab platform.

  • Vector Autoregressive Moving Average Processes
    New Introduction to Multiple Time Series Analysis, 2020
    Co-Authors: Helmut Lütkepohl
    Abstract:

    In this chapter, we extend our standard finite order VAR model, $$y_t = \nu + A_1 y_{t - 1} + \ldots + A_p y_{t - p} + \varepsilon _t , $$ by allowing the error terms, here et, to be autocorrelated rather than white noise. The autocorrelation structure is assumed to be of a relatively simple type so that et has a finite order Moving average (MA) representation, $$\varepsilon _t = u_t + M_1 u_{t - 1} + \ldots + M_q u_{t - q} ,$$ where, as usual, u t is zero mean white noise with nonsingular covariance matrix Σu. A finite order VAR process with finite order MA error term is called a VARMA (vector Autoregressive Moving average) process.

  • Linear aggregation of vector Autoregressive Moving average processes
    Economics Letters, 2002
    Co-Authors: Helmut Lütkepohl
    Abstract:

    Abstract A framework is developed in which to analyze both temporal and contemporaneous linear aggregation of vector Autoregressive Moving average processes. An aggregated finite order process is shown to be again a finite order process, and upper bounds for the Autoregressive and Moving average order are given.