Measurement error and person-specific reliability in multilevel autoregressive modeling.

The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

Gamma Autoregressive Models and Application on Kizilirmak Basin Stochastic models are required to generate synthetic values of flows statistically similar to observed ones to use in the simulation studies of water related structures. Such models are extremely important for generation of flows of streams with short record length. Autoregressive time series (AR) models are the most used models in this area. In recent years, AR models valid for gamma distribution are developed. In these studies, method of moments and modified maximum likelihood methods are used to estimate the model parameters. The aim of this study are comparing two AR models under gamma distribution assumption and applying these models for monthly data set of EIE 1501 and EIE 1517 streamflow gauging stations located in Kizilirmak Basin. The results of each model are interpreted for researchers using these models in their further studies.

Vector Autoregressive Modal Analysis With Application To Ship Structures

This article considers fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) models. The fractional differencing parameter d can be greater than 1/2, thus incorporating the important unit root case. Some sufficient conditions for stationarity, ergodicity, and existence of higher-order moments are derived. An algorithm for approximate maximum likelihood (ML) estimation is presented. The asymptotic properties of ML estimators, which include consistency and asymptotic normality, are discussed. The large-sample distributions of the residual autocorrelations and the square-residual autocorrelations are obtained, and two portmanteau test statistics are established for checking model adequacy. In particular, non-stationary FARIMA(p, d, q)-GARCH(r, s) models are also considered. Some simulation results are reported. As an illustration, the proposed model is also applied to the daily returns of the Hong Kong Hang Seng index (1983–1984).

Modal parameter identification of a multiple-span post-tensioned concrete bridge using hybrid vibration testing data

A new time series model for exponential variables having first-order autoregressive structure is presented. Unlike the recently studied standard autoregressive model in exponential variables (ear(1)), runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of directional effects in sample path behaviour. The model is further developed by the use of cross-coupling and antithetic ideas to allow negative dependency. Joint distributions and autocorrelations are investigated. A transformed version of the model has a uniform marginal distribution and its correlation and regression structures are also obtained. Estimation aspects of the models are briefly considered.

Hybrid artificial intelligence-time series models for monthly streamflow modeling

The research is dedicated in the study to time series and the ability of using fuzzy logic with it in order to develop forecasting approaches. In this research ,the time series (Autoregressive model) are linked with fuzzy logic in order to get on the Parameters of fuzzy time series models (fuzzy logic Autoregressive model),and applied that on the data of daily mistakes rates in charges production. The fuzzy Autoregressive model of time series gave forecasting more sutiable than those given by Autoregressive model.

Freshwater reservoirs play a significant role in the global carbon cycle by processing and storing large quantities of dissolved organic matter (DOM). Quantifying the magnitude of DOM fluctuations across multiple temporal scales can advance our understanding of how the controls on reservoir carbon cycling may vary. We monitored fluorescent DOM (fDOM) using an in situ epilimnetic sensor at a ten-minute resolution over one year in a eutrophic reservoir in southwestern Virginia, USA with low dissolved organic carbon concentrations (2–6 mg L−1). We determined the dominant time scales of variability and key environmental predictors of fDOM concentrations using continuous wavelet transforms and autoregressive time series modeling. Throughout the year, fDOM concentrations varied considerably, with maximum concentrations in the autumn (30.0 quinine sulfate units) and minimum concentrations in the spring (4.7 quinine sulfate units). The monthly time scale was the dominant time scale of variability, but the daily time scale was significant during the summer. Based on the autoregressive time series analysis, precipitation, water temperature, and shortwave radiation were important environmental predictors of fDOM on daily time scales, while water temperature alone best predicted monthly variability. Our study is one of the first to reveal substantial variability in fDOM concentrations during a full year, emphasizing the need for long-term, high-frequency in situ DOM monitoring to capture changes occurring on multiple time scales. By quantifying the variability and environmental predictors of fDOM on different time scales, we are able to better understand how and why DOM concentrations change throughout the year.

Measurement Errors in System Identification and the Practical Universality of Low-Order LS and AR Models

In order to evaluate the comprehensive dynamic performance of probability screen and select the appropriate working conditions, a dynamic model of probability screen vibration system is established. Then, the calculation method of the dynamic characteristic parameters, based on the time series Auto Regression (AR) model of vibration test, is used. The relationship among the comprehensive dynamic characteristics, the screening efficiency and the box dimension of probability screen vibration system is analyzed, and Least Square Support Vector Machine (LSSVM), Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to predict the screening efficiency with box dimension. The analysis result shows that the screening efficiency, the stability, the response rapidity and the comprehensive dynamic characteristic of the system are all related to the box dimension of time series. As for the complexity of probability screen vibration system, it affects the comprehensive dynamic performance, and ultimately touches the screening efficiency of the probability screen; The best working conditions for the system are selected by the curve between box dimension and the working condition parameter; Taking box dimension as the only input variable, the prediction accuracy of the screening efficiency is high by using LSSVM,GRNN and BPNN methods, the prediction results are stable and reliable, and the box dimension can be used as a single input variable to predict the screening efficiency, it has the advantages of fewer input parameters, high prediction efficiency, and high prediction accuracy, which has great potential for expanding application space and further research value.

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying lengths (T = 25, 50, 75, 100, 125) using Statistical Analysis System (SAS) version 9.2. Autoregressive components for the 5 series vectors included coefficients of .80, .70, .65, .50 and .40. Error variance components included values of .20, .20, .10, .15, and .15, with cross-lagged coefficients of .10, .10, .15, .10, and .10. A Monte Carlo study revealed that in comparison to frequentist methods, the Bayesian approach provided increased sensitivity for hypothesis testing and detecting Type I error.

not available

Scale-, time- and asset-dependence of Hawkes process estimates on high frequency price changes

This first chapter is devoted to a general introduction into the Markov-switching vector autoregressive (MS-VAR) time series model. In Section 1.2 we present the fundamental assumptions constituting this class of models. The discussion of the two components of MS-VAR processes will clarify their on time invariant vector auto-regressive and Markov-chain models. Some basic stochastic properties of MS-VAR processes are presented in Section 1.3. Finally, MS-VAR models are compared to alternative non-normal and non-linear time series models proposed in the literature. As most non-linear models have been developed for univariate time series, this discussion is restricted to this case. However, generalizations to the vector case are also considered.