To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observatio...

The well-known prediction-error-based maximum likelihood (PEML) method can only handle minimum phase ARMA models. This likelihood (BFML) method, which can handle nonminimum phase and noncausal ARMA models. The BFML method is identical to the PEML method in the case of a minimum phase ARMA model, and it turns out that the BFML method incorporates a noncausal ARMA filter with poles outside the un...

We consider computationally-fast methods for estimating parameters in ARMA processes from binary time series data, obtained by thresholding the latent ARMA process. All methods involve matching estimated and expected autocorrelations of the binary series. In particular, we focus on the spectral representation of the likelihood of an ARMA process and derive a restricted form of this likelihood, ...

A feasibility study of using of Dynamic Bayesian Networks in combination with ARMA modeling in exchange rate prediction is presented. A new algorithm (ARMA-DBN) is constructed and applied to the exchange rate forecast of RMB. Results show that the improved dynamic Bayesian forecast algorithm has better performance than the standard ARMA model.

Autoregressive moving average (ARMA) models are a fundamental tool in time series analysis that offer intuitive modeling capability and efficient predictors. Unfortunately, the lack of globally optimal parameter estimation strategies for these models remains a problem: application studies often adopt the simpler autoregressive model that can be easily estimated by maximizing (a posteriori) like...

The combination forecasting model IOWGA-EMD-ARMA-WNN is proposed in this paper. The randomness, periodicity and tendency of the original data are showed by EMD decomposition in EMD-ARMA model. WNN combines the advantages of wavelet analysis and BP neural network and improves the learning efficiency and forecasting accuracy. The weight of combination model is decided by forecasting precision of ...

OBJECTIVES AND METHODS armA is a novel plasmid-borne 16S rRNA methyltransferase that confers high-level resistance to 4,6-disubstituted deoxystreptamines. Recently, we have isolated from a high-level broad-spectrum aminoglycoside-resistant Escherichia coli animal isolate a plasmid, pMUR050, that bore the armA gene. In order to elucidate the genetic basis for the spread of armA, we have determin...

Among 235 extended-spectrum beta-lactamase-producing Klebsiella pneumoniae (ESBL) isolates collected from a nationwide surveillance performed in Taiwan, 102 (43.4%) were resistant to amikacin. Ninety-two of these 102 (90.2%) isolates were carrying CTX-M-type beta-lactamases individually or concomitantly with SHV-type or CMY-2 beta-lactamases. The armA and rmtB alleles were individually detected...

We have derived some matrix equations for speedy computation of the conditional covariance kernel of a discrete-time process obtained from irregularly sampling an underlying continuous-time ARMA process. These results are applicable to both stationary and non-stationary ARMA processes. We have also demonstrated that these matrix results can be useful in shedding new insights on the covariance s...

When we were fitting ARMA models to the data, we first looked at the sample autocovariance or autocorrelation function and we then tried to find the ARMA model whose theoretical acf matched with the sample acf. Now the sample autocovariance function is a nonparametric estimate of the theoretical autocovariance function of the process. In other words, we first estimated γ(h) nonparametrically by...