• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.457-471
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• 2023

The monthly series is an aggregation of daily values. In the absence of observable daily data, calendar effects such as trading day and holidays are estimated using a RegARIMA model. However, if the daily series were observable, these calendar effects could be estimated directly from the daily series, potentially improving the seasonal adjustment of the monthly time series. In this paper, we propose a method to improve the seasonal adjustment of monthly time series by using calendar variation estimation based on daily time series. We apply this seasonal adjustment method to three monthly time series and compare our results with those obtained using X-13ARIMA-SEATS.

• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.409-418
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• 2005

Magnetospheric substorms occur frequently during magnetic storms, suggesting that the two phenomena are closely associated. We can investigate the relation between magnetospheric substorms and magnetic storms by examining the correlation between AE and Dst indices. For this purpose, we calculated the monthly cumulative AU, $\mid{AL}\mid$ and $\mid{Dst}\mid$ indices. The correlation coefficient between the monthly cumulative $\mid{AL}\mid$ and $\mid{Dst}\mid$ index is found to be 0.60, while that between monthly cumulative AU and $\mid{Dst}\mid$ index is 0.28. This result indicates that substorms seem to contribute to the development of magnetic storms. On the other hand, it has been reported that the interplanetary electric field associated with southward IMF intensifies the magnetospheric convection, which injects charged particles into the inner magnetosphere, thus developing the ring current. To evaluate the contribution of the interplanetary electric field to the development of the storm time ring current belt, we compared the monthly cumulative interplanetary electric field and the monthly cumulative Dst index. The correlation coefficient between the two cumulative indices is 0.83 for southward IMP and 0.39 for northward IMF. It indicates that magnetospheric convection induced by southward IMF is also important in developing magnetic storms. Therefore, both magnetospheric substorm and enhanced magnetospheric convection seem to contribute to the buildup of magnetic storm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.396-399
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• 2004

어떤 집단에서든 그 구성원의 효율적인 관리가 무엇보다 중요하다. 이러한 효율적인 관리는 집단의 구성원이 많을수록 한 개인이 방대한 자료를 분석 및 관리하기에는 많은 어려움이 있다. 본 논문에서는 이 많은 자료를 바탕으로 퍼지 추론을 이용하여 근태 관리 시스템을 개발하였다. 먼저 퍼지입력 변수로는 출입구에 미리 설치되어 있는 지문 도어락과 관리실의 PC를 통해 사원들의 출퇴근 시간과 월별 근무시간, 월별 근무일수, 월별 조퇴 및 지각일수를 산출한 후 월별 휴가일수를 입력한다. 이 입력 데이터 값으로 퍼지 연산을 수행 후 구성원 개개인의 월별 성실도를 결정하였다.

• 건강소식
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• v.30 no.2 s.327
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• pp.16-17
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• 2006

건강은 대부분 새해 소원 1순위다. 병술년 한 해를 건강하게 지내기 위해서는 1년 건강 계획을 연초에 미리 짜보는 것도 바람직하다. 계절의 변화에 맞춰 매달 달라지는 질병의 유형을 제대로 파악해 대비할 수 있기 때문이다. 월별로 조심해야 할 질환과 예방 대책을 소개한다.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.155-160
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• 2014

When we collect marketing data, we can only obtain the bimonthly or quarterly data but the monthly data be available. If we evaluate or predict monthly market condition or establish monthly marketing strategies, we need to disaggregate these bimonthly or quarterly data to the monthly data. In this paper, for bimonthly or quarterly data, we introduce some methods of disaggregation to monthly data. These disaggregation methods include the simple average method, the growth rate method, the weighting method by the judgment of experts, and variable decomposition method using 12 month moving cumulative sum. In this paper, we applied variable decomposition method to disaggregate for bimonthly data of sum of electronics sales in a European country. We, also, introduce how to use this method to predict the future data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.199-213
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• 2008

The goal of this research is to develop and apply the integrational operation method (IOM) for the modeling of the monthly pan evaporation (PE) and the alfalfa reference evapotranspiration ($ET_r$). Since the observed data of the alfalfa $ET_r$ using lysimeter have not been measured for a long time in Republic of Korea, Penman-Monteith (PM) method is used to estimate the observed alfalfa $ET_r$. The IOM consists of the application of the stochastic and neural networks models, respectively. The stochastic model is applied to generate the training dataset for the monthly PE and the alfalfa $ET_r$, and the neural networks models are applied to calculate the observed test dataset reasonably. Among the considered six training patterns, 1,000/PARMA(1,1)/GRNNM-GA training pattern can evaluate the suggested climatic variables very well and also construct the reliable data for the monthly PE and the alfalfa $ET_r$. Uncertainty analysis is used to eliminate the climatic variables of input nodes from 1,000/PARMA(1,1)/GRNNM-GA training pattern. The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. Finally, it can be to model the monthly PE and the alfalfa $ET_r$ simultaneously with the least cost and endeavor using the IOM.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.93-101
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• 2017

The distribution of inter-annual variation in temperature would help evaluate the likelihood of a climatic risk and assess suitable zones of crops under climate change. In this study, we evaluated two methods to estimate the standard deviation of temperature in the areas where weather information is limited. We calculated the monthly standard deviation of temperature by collecting temperature at 0600 and 1500 local standard time from 10 automated weather stations (AWS). These weather stations were installed in the range of 8 to 1,073m above sea level within a mountainous catchment for 2011-2015. The observed values were compared with estimates, which were calculated using a geospatial correction scheme to derive the site-specific temperature. Those estimates explained 88 and 86% of the temperature variations at 0600 and 1500 LST, respectively. However, it often underestimated the temperatures. In the spring and fall, it tended to had different variance (e.g., increasing or decreasing pattern) from lower to higher elevation with the observed values. A regression analysis was also conducted to quantify the relationship between the standard deviation in temperature and the topography. The regression equation explained a relatively large variation of the monthly standard deviation when lapse-rate corrected temperature, basic topographical variables (e.g., slope, and aspect) and topographical variables related to temperature (e.g., thermal belt, cold air drainage, and brightness index) were used. The coefficient of determination for the regression analysis ranged between 0.46 and 0.98. It was expected that the regression model could account for 70% of the spatial variation of the standard deviation when the monthly standard deviation was predicted by using the minimum-maximum effective range of topographical variables for the area.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.81-90
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• 2008

Bus passengers need some time to adapt to the changed bus route or free bus transfer system which is part of the public transportation system restructuring plan. This research is focused on the characteristics of monthly patterns of bus passengers. The period of stabilization of bus passenger demand after the rearrangement of bus route system by a time series were analysed. In order to look into the characteristics of bus passenger demand by month, data on the number of monthly bus passengers of recent five years in metropolitan cities across the nation was collected. Kendall's coefficient of concordance is used to test whether the cities showed concordance with respect to the number of monthly bus passengers during a period of five years. The study collected and performed a time series analysis of data on the number of monthly bus passengers during the past ten years in Daegu metropolitan area which carried out a new bus route plan in February 2006. The number of monthly bus passengers in 2006 was estimated using the time series analysis. The city of Daegu found that after six months the estimated and actual values displayed a similar pattern. This result can be applied to other cities in estimating the passenger demands in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.571-577
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• 2019

Since the introduction of digital cameras in an aerial-photogrammetry field on 2006, the technological paradigm related to the photogrammetry has been shifting from the analog types to digital types. However, current construction standard cost for the aerial-photogrammetry and the digital mapping are being mixed with analog-based concepts and digital-based methods. In the current standard cost, the monthly weather table is closely related to the calculation of the number of flying days in a taking of aerial photograph. The current monthly weather table uses the results calculated from the observation data of total cloud amount from 1999 to 2007. In this study, the monthly weather table was calculated using the total cloud data during ten years from 2009 to 2018. As a result, the newly calculated number of clear days for 29 stations was analyzed as 44 days decreased by 6 days. The maximum number of clear days decreased in Jinju as 23 days, and the highest decreased clearing days was February.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.27-37
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• 2017

Enrollment rate of childcare centers is an important indicator that regulates supply and demand of childcare services in South Korea, but monthly enrollment rate difference of childcare centers has not been studied. This study examines monthly enrollment rate difference from March 2015 to February 2016. Data for this study is drawn from the Korean Childcare Centers database and includes information about 39,775 childcare centers. Enrollment rate had increased steadily from 71.8% in March 2015 to 84.4% in January 2016, whereas it decreased by 50.3% in February 2016. The result showed that enrollment rate difference between March 2015 and January 2016 is about 13%p, and even those of between January and February 2016 is 32%p. Taken together, these findings suggest that the supply and demand of childcare services need to be regulated based on monthly enrollment information.