BACKGROUND Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time. METHODOLOGY We propose a statistical method for functional clustering of periodic gene expression by modeling the covariance matrix of serial mea...

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 ...

Long intermediate AR models are used in Durbin's algorithms for ARMA estimation. The order of that long AR model is infinite in the asymptotical theory, but very high AR orders are known to give inaccurate ARMA models in practice. A theoretical derivation is given for two different finite AR orders, as a function of the sample size. The first is the AR order optimal for prediction with a purely...

We address the problem of estimating the motion of a wide-band source from single passive sensor measurements, for example, estimation of the speed and position of a car moving on a road from the recording of its acoustic signature at a microphone located next to the road. We present a new computationally efficient method based on a time-varying ARMA model for Doppler-shifted random processes. ...

A procedure is proposed for computing the autocovariances and the ARMA representations of the squares, and higher-order powers, of Markov-switching GARCH models. It is shown that many interesting subclasses of the general model can be discriminated in view of their autocovariance structures. Explicit derivation of the autocovariances allows for parameter estimation in the general model, via a G...

The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate d...

7. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 8. Supplemental Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8.1. Description of Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 8.2. Assumption V1 for Vector β . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 ...

We consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to so-called all-pass models in that it allows for autocorrelation and for more flexible forms of conditional heteroskedasticity. These features may be attractive especially in economic and financial application...

Nonstationary ARIMA processes and nearly nonstationary ARMA processes, such as autoregressive processes having a root of the AR polynomial close to the unit circle, have sample autocovariance and spectral properties that are, in practice, almost indistinguishable from those of a stationary longmemory process, such as a Fractionally Integrated ARMA (ARFIMA) process. Because of this, model misspe...