Time Series Analysis of Ultrasonic Observations of Gross Fetal Body Movements during the Last 10 Weeks of Pregnancy

1981 ◽  
Vol 3 (4) ◽  
pp. 330-341 ◽  
Author(s):  
Karen Campbell ◽  
Ian MacNeill ◽  
John Patrick
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Time Series Analysis ◽  
Regression Model ◽  
Body Movement ◽  
Frequency Range ◽  
Body Movements ◽  
Significant Power ◽  
First Order ◽  
Auto Regression

Thirty fetuses were observed for 24 hours and one fetus was observed for 20 hours during the last 10 weeks of gestation. Observations were made of the amount of gross fetal body movement in each successive 5 minute observation epoch, thus resulting in 30 time series of 288 observations and one time series of 240 observations. Spectral analysis of these time series demonstrated the presence of significant power in the frequency range of 0.002 to 0.0175 cpm. Application of Box-Jenkins techniques to the time series resulted in the choice of a first-order auto-regression model to fit the data. It was concluded that the incidence of episodes of gross fetal body movements were non-random and were, in fact, pseudoperiodic.

Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

2018 ◽  
Vol 233 (7) ◽  
pp. 701-714
Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
High Speed ◽  
Support Vector ◽  
High Speed Train ◽  
Support Vector Regression Model ◽  
Vibration Time ◽  
Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

scholarly journals Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Sun-Hee Kim ◽  
Christos Faloutsos ◽  
Hyung-Jeong Yang
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Regression Model ◽  
Random Coefficients ◽  
Experimental Results ◽  
Ar Model ◽  
Forecasting Method ◽  
Eeg Data ◽  
Future Value ◽  
Auto Regression

Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with−1and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately.

Intraindividual reference values

2004 ◽  
Vol 42 (7) ◽  
Author(s):  
Josep M. Queraltó
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Statistical Significance ◽  
Reference Intervals ◽  
First Order ◽  
Series Analysis ◽  
Statistical Framework ◽  
Reliable Model ◽  
Key Concepts ◽  
High Degree

AbstractWhen a biological quantity examination exhibits a high degree of individuality, developing a strategy for interpreting these values in an individual context can be a useful alternative. Time-series analysis is the appropriate statistical framework to build a model for explanation of the behaviour of laboratory information and to forecast future values. The key concepts in this approach are autocorrelation and withinperson variance. Unfortunately, the powerful tools provided by time-series analysis require many observations, a requisite difficult to meet in every day practice. However, introducing some restrictions in the autocorrelation parameter of the most reliable model, the first order autocorrelation model, and using the average within-person variance from a selected population, it is possible to build predictive reference intervals for an individual, based on only few observations. The most common case is the minimum time series: when there are just two observations. The statistical significance of the change from a previous observation is a problem that arises from both quality control (delta checks) and the interpretative diagnostic fields (reference change limit). Applying the same restrictive criteria, it is possible to develop specific limits for a difference between consecutive observations based on a within-person variance selected from the distribution of variances found in a sample of similar individuals.

scholarly journals Research of New Criteria for Detecting First-order Residuals Autocorrelation in Regression Models

2018 ◽  
pp. 13-25
Author(s):  
M. P. Bazilevsky
Keyword(s):  
Mathematical Programming ◽  
Regression Model ◽  
Least Squares ◽  
Regression Models ◽  
Linear Constraints ◽  
Mathematical Programming Problem ◽  
First Order ◽  
Watson Statistic ◽  
Auto Regression ◽  
New Criteria

When estimating regression models using the least squares method, one of its prerequisites is the lack of autocorrelation in the regression residuals. The presence of autocorrelation in the residuals makes the least-squares regression estimates to be ineffective, and the standard errors of these estimates to be untenable. Quantitatively, autocorrelation in the residuals of the regression model has traditionally been estimated using the Durbin-Watson statistic, which is the ratio of the sum of the squares of differences of consecutive residual values to the sum of squares of the residuals. Unfortunately, such an analytical form of the Durbin-Watson statistic does not allow it to be integrated, as linear constraints, into the problem of selecting informative regressors, which is, in fact, a mathematical programming problem in the regression model. The task of selecting informative regressors is to extract from the given number of possible regressors a given number of variables based on a certain quality criterion.The aim of the paper is to develop and study new criteria for detecting first-order autocorrelation in the residuals in regression models that can later be integrated into the problem of selecting informative regressors in the form of linear constraints. To do this, the paper proposes modular autocorrelation statistic for which, using the Gretl package, the ranges of their possible values and limit values were first determined experimentally, depending on the value of the selective coefficient of auto-regression. Then the results obtained were proved by model experiments using the Monte Carlo method. The disadvantage of the proposed modular statistic of adequacy is that their dependencies on the selective coefficient of auto-regression are not even functions. For this, double modular autocorrelation criteria are proposed, which, using special methods, can be used as linear constraints in mathematical programming problems to select informative regressors in regression models.

scholarly journals The Impact of Globalization on Economies of Developed Countries

2013 ◽  
Vol 2 (3) ◽  
pp. 25 ◽  
Author(s):  
Lenka Pelegrinová ◽  
Martin Lačný
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Time Series Analysis ◽  
Regression Model ◽  
Nonparametric Regression ◽  
Developed Countries ◽  
Global Level ◽  
Nonparametric Regression Model ◽  
Macroeconomic Indicators ◽  
The Impact

The concept of globalization is interpreted by various authors in terms of its importance or content. This article presents results of an analysis of the influence of globalization trends on important macroeconomic indicators of selected countries. An examination of the level of globalization as a quantitative marker was enabled by the KOF Index of Globalization, which provides an indication of the economic, political and social globalization at global level. Research methods included time series analysis, trend analysis and nonparametric regression model (regression of panel data).

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