scholarly journals Reliability Estimation Based on the Degradation Amount Distribution Using Composite Time Series Analysis and Grey Theory

2013 ◽  
Vol 13 (3) ◽  
pp. 3-14
Author(s):  
Li Wang, ◽  
Zaiwen Liu ◽  
Xuebo Jin ◽  
Yan Shi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Estimation Method ◽  
Reliability Evaluation ◽  
Reliability Estimation ◽  
Grey Theory ◽  
Series Analysis ◽  
Electronic Product ◽  
Random Failure ◽  
Random Failure Threshold

Abstract This paper puts forward a reliability estimation method by the Degradation Amount Distribution (DAD) of products, using a composite time series modeling procedure and grey theory based on a random failure threshold. Product DAD data are treated as a composite time series and described using a composite time series model to predict a long-term trend of degradation. The degradation test is processed for a certain electronic product and the degradation data is collected for reliability estimation. Comparison among the reliability evaluation by DAD composite time series analysis and grey theory, based on a constant and a random failure threshold, reliability evaluation by DAD regression analysis based on a random failure threshold, reliability evaluation by degradation path time series analysis, and real reliability of the electronic product is done. The results show that the reliability evaluation of the product using the method proposed is the most creditable of all.

A Study on Surface Evaporation in Liaoyang Based on Grey Theory

2014 ◽  
Vol 675-677 ◽  
pp. 875-879
Author(s):  
Jin Liang Chen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Climate Models ◽  
Visual Basic ◽  
Liaoning Province ◽  
Forecasting Model ◽  
Grey Theory ◽  
Surface Evaporation ◽  
Series Analysis ◽  
Grey Forecasting

At present, there are mainly two methods to forecast the quantity of surface evaporation: one is by time series analysis, another is by climate models. This paper established models to simulate the surface evaporation in Liaoyang based on grey theory, and developed a grey forecasting software for surface evaporation using Visual Basic. From the annual depreciation from 1998 to 2010 in Liaoyang station in Liaoning Province, a grey forecasting model was established, which was then used to predict the quantity of surface evaporation in Liaoyang from 2011 to 2015.

scholarly journals OPTIMASI PARAMETER MODel AUTOREGRESSIVE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION

2019 ◽  
Vol 8 (2) ◽  
pp. 208-219
Author(s):  
Setyoko Prismanu Ramadhan ◽  
Hasbi Yasin ◽  
Suparti Suparti
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Arima Model ◽  
Estimation Method ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Ar Model ◽  
Series Data ◽  
Series Analysis

Box-Jenkins ARIMA method is a linear model in time series analysis which is widely used in various fields. One estimation method for Box-Jenkins ARIMA model is OLS method which aims to minimize the number of squared errors. This method is not effective when applied to time series data that is random, nonlinear and non-stationary. In this study discussed the alternative method of the PSO algorithm as an parameter optimization of the ARIMA model. PSO algorithm is an optimization method based on the behavior of a flock of birds or fish. The main advantage of the PSO algorithm is having a simple, easy to implement and efficient concept in calculations. This method is applied to data from PT Perusahaan Gas Negara shares. The results of both methods will be compared. In the AR model (1) the value of MSE is 0.532 and MAPE is 0.993. Meanwhile, the PSO algorithm obtained MSE 0.531 and MAPE 0.988. It was found that the PSO algorithm resulted in smaller MSE and MAPE values and could provide better results.Keywords : Time Series Analysis, Autoregressive, PSO

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

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