Periodic moving average process

Modeling process asymmetries with Laplace moving average

Many records in environmental science exhibit asymmetries: for example in shallow water and with variable bathymetry, the sea wave time series shows front–back asymmetries and different shapes for crests and troughs. In such situation, numerical models are available but their computational cost and complexity are high. A stochastic process aimed at modeling such asymmetries has recently been pr...

Forecasting with prediction intervals for periodic autoregressive moving average models

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 pred...

Dual-component model of respiratory motion based on the periodic autoregressive moving average (periodic ARMA) method.

A new approach to the problem of modelling and predicting respiration motion has been implemented. This is a dual-component model, which describes the respiration motion as a non-periodic time series superimposed onto a periodic waveform. A periodic autoregressive moving average algorithm has been used to define a mathematical model of the periodic and non-periodic components of the respiration...

Complete Convergence for Moving Average Process of Martingale Differences

A Moving Average Exponential Point Process ( Ema 1 )

A construction is given for a stationary sequence of random variables {X.} which have exponential marginal distributions and are random linear combinations of order one of an i.i.d. exponential sequence {e.}. The Joint and trivariate exponential distributions of X.,, X. and X... are studied, as well as the Intensity function, point spectrum and variance time curve for the point process which ha...