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 ...

INTRODUCTION Costs, length of hospital staying and morbidity are frequently and significantly increased as a result of infections and other complications following surgical procedure for gastrointestinal tract cancer. Recently, improving host defence mechanisms have become a target of interest. Immunonutrition aims at improving immunity, most likely providing key nutrients to maintain T-lymphoc...

This paper investigates the asymptotic theory for a vector autoregressive moving average–generalized autoregressive conditional heteroskedasticity ~ARMAGARCH! model+ The conditions for the strict stationarity, the ergodicity, and the higher order moments of the model are established+ Consistency of the quasimaximum-likelihood estimator ~QMLE! is proved under only the second-order moment conditi...

There is a great demand for statist ical modeling of phenomena tha t evolve in bo th space and time. Practical examples are those in Haslett and Raf tery (1989), Handcock and Wallis (1994), Cressie and Huang (1999), Brix and Diggle (2001), Stroud et al. (2001), De Iaco et al. (2002), Gneit ing (2002), and Hartfield and Gunst (2003), to mention but a few. Two commonly used tools to describe the ...

This paper presents a unified framework of stationary ARMA processes for discrete-valued time series based on Pegram’s [Pegram, G.G.S., 1980. An autoregressive model for multilag markov chains. J. Appl. Probab. 17, 350–362] mixing operator. Such a stochastic operator appears to be more flexible than the currently popular thinning operator to construct Box and Jenkins’ type stationary ARMAproces...

Using a perturbation matrix, we introduced a hyperplane used to define a generalized null-spectrum, based on both the signal and noise subspaces, while the MUSIC and Min-Norm null-spectra are defined based only on the noise subspace. With the generalized nullspectrum, we derived the upper and lower bounds of a class of the generalized null-spectrum, called the maximum and minimum null-spectra, ...

In the context of carbon neutrality and air pollution prevention, it is great research significance to achieve high-accuracy prediction quality index. this paper, Beijing used as study area; data from January 2014 December 2019 are training set, 2020 2021 test set. The CEEMDAN-ARMA-LSTM model constructed in paper for analysis. CEEMDAN decompose improve information utilization. smooth non-white ...