• Korean Management Science Review
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• v.20 no.1
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.439-447
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• 2021

Due to the frequent emergence of global abnormal climates, related studies on meteorological change is being actively proceed. However, the research on trend analysis using weather data accumulated over a long period of time was insufficient. In this study, the trend of temperature time series data accumulated from automated surface observing system (ASOS) for 40 years was analyzed by using a non-parametric analysis method. As a result of the Mann-Kendall test on the annual average temperature and seasonal average temperature time series data in South Korea, it has shown that an upward trend exists. In addition, the result of calculating the Sen's slope, which can determine the degree of tendency before and after the searched change point by applying the Pettitt test, recent data after the fluctuation point confirmed that the tendency of temperature rise was even greater.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.765-777
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• 2009

Our study aimed to illustrate long term trend in 10 micrometer particular matters excluding confounding effect. Daily 10 micrometer particular matters data were measured in 27 places and meteorological data (maximum temperature, humidity and maximum wind speed, solar radiation) were obtained from the national institute of environmental research for the period from January, 1996 to December 2000. To estimate the increasing and decreasing long term trend in a set of observed data, set up the model. The model included regression spline smooth function on the time and meteorological factors to capture the seasonal time trend and any possible nonlinear relationship. The result was estimated to decrease slightly after adjusting for meteorological factors and seasonal time trend.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.597-612
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• 2001

We have developed a \

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.67-90
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• 1998

In this paper we analytically study model misspecification that arises in regression analysis of nonstationary seasonal time series. We assume the underlying data generating process is a seasonally or a regularly and seasonally integrated process. We first study consequences of totally misspecified cases where seasonal indicator variables, a linear time trend, or another statistically independent seasonally integrated process are used as predictor variables in order to model the nonstationary seasonal behavior of the dependent variable. Then we study consequences of partially misspecified cases where the dependent variable and a predictor variable are cointegrated at some, but not all of the frequencies corresponding to the nonstationary roots.

• Journal of Applied Reliability
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• v.3 no.1
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• pp.13-40
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

• Journal of Wetlands Research
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• v.24 no.4
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• pp.301-311
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• 2022

The purpose of this study is to analyze the seasonal characteristics of water quality using long-term water quality monitoring data. Seasonal characteristics of water quality were analyzed using monitoring data from 34 tributaries where long-term monitoring was performed in the Nakdong River system, and average data analysis of water quality, coefficient of variation analysis, and trend analysis were performed for seasonal analysis. For seasonal analysis, average data analysis of water quality, coefficient of variation analysis, and trend analysis were performed. As a result of the evaluation of the coefficient of variation, tributaries were larger than main streams, and BOD, T-P, and TOC were larger in autumn and T-N were larger in spring. Trend analysis was analyzed using Mann-Kendall and Sen's Slope. BOD, T-N, and T-P tended to decrease, but TOC had a lot to increase. Through this study, it was possible to evaluate the availability of long-term water quality monitoring data and analyze seasonal characteristics, and to analyze the stabilization period of water quality and changes in pollutant sources for watershed management.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.21-29
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• 2019

This study analyzed the variability of drought risk based on trend analysis of dry-seasonal dam inflow located in upper river basins. To this, we used areal averaged precipitation and dam inflow of three upper river dams such as Soyang dam, Chungju dam, and Andong dam. We employed Mann-Kendall trend analysis and change point detection method to identify the significant trends and changing point in time series. Our results showed that significant decreasing trends (95% confidence interval) in dry-seasonal runoff rates (= dam inflow/precipitation) in three-dam basins. We investigated potential causes of decreasing runoff rates trends using changes in potential evapotranspiration (PET) and precipitation indices. However, there were no clear relation among changes in runoff rates, PET, and precipitation indices. Runoff rate reduction in the three dams may increase the risk of dam operational management and long-term water resource planning. Therefore, it will be necessary to perform a multilateral analysis to better understand decreasing runoff rates.

• Membrane and Water Treatment
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• v.10 no.5
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• pp.339-352
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• 2019

Long term water quality change was analyzed to evaluate the effect of the Total Maximum Daily Load (TMDL) policy. A trend analysis was performed for biochemical oxygen demand (BOD) and total phosphorus (TP) concentrations data monitored at the outlets of the total 41 TMDL unit watersheds of the Nakdong River in the Republic of Korea. Because water quality data do not usually follow a normal distribution, a nonparametric statistical trend analysis method was used. The monthly mean values of BOD and TP for the period between 2004 and 2015 were analyzed by the seasonal Mann-Kendall test and the locally weighted scatterplot smoother (LOWESS). The TMDL policy effect on the water quality change of each unit watershed was analyzed together with the results of the trend analysis. From the seasonal Mann-Kendall test results, it was found that for BOD, 7.8 % of the 41 points showed downward trends, 26.8 % and the rest 65.9% showed upward and no trends. For TP, 51.2% showed no trends and the rest 48.8% showed downward trends. From the LOWESS analysis results, TP began to decrease in most of the unit watersheds from mid-2010s when intensive chemical treatment processes were introduced to existing wastewater treatment plants. Overall, for BOD, relatively more points were improved in the main stream compared to the points of the tributaries although overall trends were mostly no trend or upward. For TP, about half of the points were improved and the rest showed no trends.

• Journal of Environmental Science International
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• v.26 no.2
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• pp.221-230
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• 2017

To determine the effect of air pollution reduction policies, the long-term trend of air pollutants should be analyzed. Kolmogorov-Zurbenko (KZ) filter is a low-pass filter, produced through repeated iterations of a moving average to separate each variable into its temporal components. The moving average for a KZ(m, p) filter is calculated by a filter with window length m and p iterations. The output of the first pass subsequently becomes the input for the next pass. Adjusting the window length and the number of iterations makes it possible to control the filtering of different scales of motion. To break down the daily mean $PM_{10}$ into individual time components, we assume that the original time series comprises of a long-term trend, seasonal variation, and a short-term component. The short-term component is attributable to weather and short-term fluctuations in precursor emissions, while the seasonal component is a result of changes in the solar angle. The long-term trend results from changes in overall emissions, pollutant transport, climate, policy and/or economics. The long-term trend of the daily mean $PM_{10}$ decreased sharply from $59.6ug/m^3$ in 2002 to $44.6ug/m^3$ in 2015. This suggests that there was a long-term downward trend since 2005. The difference between the unadjusted and meteorologically adjusted long-term $PM_{10}$ is small. Therefore, we can conclude that $PM_{10}$ is unaffected by the meteorological variables (total insolation, daily mean temperature, daily mean relative humidity, daily mean wind speed, and daily mean local atmospheric pressure) in Busan.