Time series analysis with graph-based semi-supervised learning

2015 ◽  
Author(s):  
Zhao Xu ◽  
Koichi Funaya
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Supervised Learning ◽  
Series Analysis

scholarly journals Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1068
Author(s):  
Ji-hye Jun ◽  
Tai-Woo Chang ◽  
Sungbum Jun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Supervised Learning ◽  
Manufacturing Process ◽  
Continuous Flow ◽  
Classification Model ◽  
Production Plan ◽  
Continuous Manufacturing ◽  
Series Analysis ◽  
Discrete Manufacturing

Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure.

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.

The Contribution of Foreign Direct Investment for Economic Growth of Ethiopia: Time Series Analysis

2018 ◽  
Vol 9 (02) ◽  
Author(s):  
Addissie Melak
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Time Series Analysis ◽  
Direct Investment ◽  
Policy Framework ◽  
Annual Growth Rate ◽  
African Countries ◽  
Series Analysis ◽  
Integration Test

Economic growth of countries is one of the fundamental questions in economics. Most African countries are opening their economies for welcoming of foreign investors. As such Ethiopia, like many African countries took measures to attract and improve foreign direct investment. The purpose of this study is to examine the contribution of foreign direct investment (FDI) for economic growth of Ethiopia over the period of 1981-2013. The study shows an overview of Ethiopian economy and investment environment by the help of descriptive and econometric methods of analysis to establish empirical investigation for the contribution of FDI on Ethiopian economy. OLS method of time series analysis is employed to analyse the data. The stationary of the variables have been checked by using Augmented Dickey Fuller (ADF) Unit Root test and hence they are stationery at first difference. The co- integration test also shows that there is a long run relationship between the dependent and independent variables. Accordingly, the finding of the study shows that FDI, GDP per capita, exchange rate, total investment as percentage of GDP, inflow of FDI stock, trade as percentage of GDP, annual growth rate of GDP and liberalization of the economy have positive impact on Ethiopian GDP. Whereas Gross fixed domestic investment, inflows of FDI and Gross capital formation influence economic growth of Ethiopia negatively. This finding suggests that there should be better policy framework to attract and improve the volume of FDI through creating conducive environment for investment.

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