The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent research activities in forecasting with art...

For a given time series observation sequence, we can estimate the parameters of the AutoRegression Moving Average (ARMA) model, thereby representing a potentially long time series by a limited dimensional vector. In many applications, these parameter vectors will be separable into different groups, due to the different underlying mechanisms that generate differing time series. We can then use c...

This paper investigates the issue on how to effectively model time series with a new algorithm given by a Multilayer Feedforward Neural Network (MLFNN) and an Autoregressive Moving Average (ARMA). The static nonlinear part is modeled by MLFNN, and the linear part is modeled by an ARMA model. The algorithm is developed for estimating the weights of the MLFNN and the parameters of ARMA model. To ...

Abstract Dairy sector is one of the fastest growing sectors in world with little global contributions from African countries and Nigeria particular. This study modelled forecast diary milk production Iwo its environs using different variants Autoregressive Moving Average (ARMA) models. Data used this comprised daily between 26th May, 2021 31st 2022 as obtained Bowen University collection centre...

JAMES ROCHON. Inference from the Incomplete Longitudinal Design under an ARMA Cdvariance Structure (Under the direction of RONALD W. HELMS). A stochastic model is presented for the analysis of the longitudinal design, appropriate when some of the response variables are missing. The general linear model is used to relate these dependent variables to other variables which are thought to account f...

This paper established the ARMA-GJR-AL model of dynamic risk VaR and CVaR measurement. Considering from aspects of the correlation and volatility and residual distribution characteristics, studying the dynamic risk measures of VaR and CVaR based on ARMA-GJR-AL model. Through empirical research, Risk prediction and accuracy of inspection are given of the Shanghai stock market and the New York st...

The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the ...

We show how the ARMA-Power GARCH model for the conditional mean and variance can be adapted to analyze times series data showing asymmetry. Dynamics is introduced in the location and the dispersion parameters of skewed location-scale distributions using the same type of structure found in the conditional mean and in the conditional variance in the ARMA-APARCH model. We also propose a general dy...