• Convergence Security Journal
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• v.8 no.2
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• pp.35-40
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• 2008

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. The used software failure time data for forecasting failure time is random number of Weibull distribution(shaper parameter 1, scale parameter 0.5), Using this data, we are proposed to ARIMA(AR(1)) and simulation method for forecasting failure time. The practical ARIMA method is presented.

• Convergence Security Journal
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• v.12 no.3
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• pp.115-121
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• 2012

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offers information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, we predict the future failure time by using the curve regression analysis where the s-curve, growth, and Logistic model is used. The proposed prediction method analysis used failure time for the prediction of this model. Model selection using the coefficient of determination and the mean square error were presented for effective comparison.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.21-27
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• 2021

The financial sector is one of the most important building blocks of the economy. When this sector efficiently implemented a well-crafted program on banking and financial system to translate financial activities to income-generating activity, economic growth will be realized. Hence, this study analyzed the effect of financial intermediation on economic growth and the existence of cointegrating relationship using time-series data from 1986 to 2015. The influence of financial intermediation in terms of bank credit to bank deposit ratio, private credit, and stock market capitalization and time trend to economic growth was estimated using ordinary least squares (OLS) multiple regression. The results showed that all the financial intermediation indicators and time trend exert significant effect on Gross Domestic Product (GDP) per capita. The positive sign of the time trend indicates that there is an upward trend in GDP per capita averaging approximately 0.06 percent annually. Furthermore, the cointegration test using the Johansen procedure revealed that there is a presence of long-term equilibrium relationship between financial intermediation and time trend and economic growth, and rules out spurious regression results. This study established the idea that financial intermediation in the Philippines has a significant and vital role in stimulating growth in the economy.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.193-193
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• 2017

Trend analysis can enhance our knowledge of the dominant processes in the area and contribute to the analysis of future climate projections. The results of previous studies in South Korea showed that southeast regions of Korea had the highest value of evapotranspiration. Thereby, it is of interest to determine the trend analysis in hydrological variables in this area. In this study, the recent 35 year trends of precipitation, reference evapotranspiration, and aridity index in monthly and annual time scale will be analyzed over three stations (Pohang, Daegu, and Pusan) of southeast Korea. After removing the significant Lag-1 serial correlation effect by pre-whitening, non-parametric statistical Mann-Kendall test was used to detect the trends. Also, the slope of trend of the Mann-Kendall test was determined by using Theil-Sen's estimator. The results of the trend analysis of reference evapotranspiration on the annual scale showed the increasing trend for the three mentioned stations, with significant increasing trend for Pusan station. The results obtained from this research can guide development if water management practices and cropping systems in the area that rely on this weather stations. The approaches use and the models fitted in this study can serve as a demonstration of how a time series trend can be analyzed.

• Journal of Korean Society on Water Environment
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• v.26 no.6
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• pp.895-902
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• 2010

The effect of seasonality on water quality variation is very significant. Generally, it reduce the power of the trend extraction. A parametric time-series model was used for detecting trends in historic constituent concentration data. The effect of seasonality is able to remove from time series decomposition technique. According to such statistic methode, long-term water quality trend analysis system (NTrend 1.0) was developed by Nakdong River Water Environmental Research Center. The trend analysis of BOD variation was conducted with NTrend 1.0 at Goreong and Moolkum site in Nakdong river to show the effect of water quality management action plan. Power test of trend extraction was tried each case of 'deseasonalized and deannulized' data and 'deseasonalized' data. Analysis period was from 1989 to 2006, and it's period was divided again three times, 1989~1993, 1994~1999 and 2000~2006 according to action plan period. The BOD trend was downward in Goreong site during three times and it's trend slope was very steep, and upward in Moolkum during 1989~1993, but it was turned downward during 1994~1999 and 2000~2006. It was revealed that it's very effective to reduce the concentration of BOD by water quality management action plan in that watershed. The result of power test was shown that it is high for trend extraction power in case of 'deseasonalized' data.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1421-1426
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• 2016

Background: There is a considerable lack of understanding of oral cancer incidence, especially its time trend in Iran. In this study, the authors aimed to analyze time trend of oral cancer incidence with a focus on differences by gender in a period of six years - from 2005 to 2010. Materials and Methods: Both population-based cancer registry and national cancer registry (NCR) data based on pathologic reports from 2005 to 2010 were obtained from the Ministry of Health and Medical Education (MOHME). Population data were also received from Statistical Centre of Iran. Age-standardized incidence rates (ASRs) based on the World Standard Population were then calculated. Finally, Negative Binomial regression was run for time trend analysis. Results: The maximum ASR for males was calculated as 2.5 per 100,000 person-years in 2008 and the minimum was observed as 1.9 per 100,000 person-years in 2005 and 2006. Meanwhile, the maximum ASR for females was estimated as 1.8 per 100,000 person-years in 2009 and the minimum was calculated as 1.6 per 100,000 person-years in 2005 and 2006. Additionally, in females, incidence risk ratio (IRR) did not show a clear decreasing or increasing trend during the six years. Nevertheless, in males an increasing trend was observed. The maximum IRR adjusted for age group and province, for females was reported in 2009 (IRR=1.05 95% CI: 0.90-1.23), and for males was estimated in 2010 (IRR=1/2 95% CI: 1.04 - 1.38). Conclusions: Our findings highlight disparities between oral cancer incidence trends in males and females over the six years from 2005 to 2010.

• Journal of Environmental Impact Assessment
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• v.12 no.1
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• pp.45-54
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• 2003

The purpose of this paper is to examine the trend on the change of the cherry blossom flowering time due to the temperature change by selecting regions that have long periods of cherry blossom flowering time data as cases. With the flowering time data, the distribution of cherry blossom flowering time, time series change and change rate of cherry blossom flowering time were analyzed. Also, the correlation between the cherry blossom flowering time and the temperature was analyzed. The flowering of cherry blossom is earlier in metropolitan areas, and in the east coastal region than the west coastal region. The trend on the change of the cherry blossom flowering time is very similar to change the temperature. The change rate of the cherry blossom flowering time is rising up in the whole regions under study, and is relatively high in metropolitan areas. Especially, the cherry blossom flowering time festinated greatly in Pohang that is one of the heavily industrialized cities. From the analysis of correlation analysis between cherry blossom flowering time and temperature elements, the cherry blossom flowering time is highly related with mean temperature of March, which the month is just before the beginning of flowering.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.353-371
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• 2022

This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

• Journal of Korea Water Resources Association
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• v.46 no.3
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• pp.219-227
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• 2013

Time series data including change, jump, trend and periodicity generally have nonstationarity. Especially, various methods have been proposed to identify the trend about hydrological time series data. However, among various methods, evaluation about capability of each trend test has not been done a lot. Even for the same data, each method may show the different result. In this study, the simulation was performed for identification about the changes in trend analysis according to the statistical characteristics and the capability in the trend analysis. For this purpose, power test for the trend analysis is conducted using Men-Kendall test, Hotelling-Pabst test, t test and Sen test according to the slope, sample size, standard deviation and significance level. As a result, t test has higher statistical power than the others, while Mann-Kendall, Hotelling-Pabst, and Sen tests were similar results.

• Korean Institute of Interior Design Journal
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• v.21 no.2
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• pp.84-91
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• 2012

The total data of observing interior space was divided into a few time frames for analysis. If we can understand the changing process of observation degree as the observation time passes, we will be able to analyse the characteristic and process of information obtainment in the case of space observation. For this purpose, the observation time was parted into 30 second units and the changing characteristic by time frame and observation area was analysed. The conclusion derived from this study is as the following: First, analysis of observation frequency and time on the basis of the average data of each subject showed that the observation time increased compared with the subject's frequency and the overall trend but that it was difficult for me to think there was a certain trend in the observation time of each subject. However, when I examined the time change by using the trend line which is a dynamic average line representing the observation time from the subjects as the trend element of time series, I could see the trend that the subject's observation time increased at a fixed rate as the frequency increased. Second, when I compared and analysed the average observation area at Area I set up by the time of 30 second unit and the observation area of Area I from the all data, I could see that the former had more degree of concentration at Area I. This analysis enabled me to get the degree of concentration on the observed area every time, and accordingly I could also see that when the data of intensive observation by time frame is analysed, the degree of concentration is dispersed for the subjects to observe very intensively or the area with overlapping observations each time frame can be seen as Area I out of the entire observation data. Third, when I analysed the observation characteristics by time frame at the 6 areas divided at 30 second unit at the rate of the number to the time of observation areas, I could see that as the observation time passed while the number of the observation areas defined as decreased the observation time increased, which means that when the area numbers decreases the area intensively observed by the subjects decreases as the time passes. In spit of that, the increase of time can be interpreted as more intensive observation of a specific area.