• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.13-21
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• 2011

This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.205-220
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• 2016

Seasonal adjustment is useful to provide a better understanding of underlying trends in Korean economic statistics. The seasonal component also includes calendar effects such as Seol and Chuseok. Most popular seasonal adjustment methods are X-12-ARIMA of the U.S. Bureau of the Census and TRAMO-SEATS of the Bank of Spain. Statistics Korea and the Bank of Korea compile seasonally adjusted series of several Korean economic statistics. This paper illustrates basic principles for seasonal adjustment and the current status of seasonal adjustment in Korea based on previous research. In addition, several issues on seasonal adjustment are addressed.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.885-893
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• 2015

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1434-1438
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• 2010

수문자료의 계절성은 수자원관리의 관점에서 매우 중요한 요소로서 계절성의 변동은 댐의 운영, 홍수조절, 관계용수 관리 등 다양한 분야와 밀접한 관계를 가지고 있다. 그러나 지금까지의 수문 자료의 계절성 평가는 주로 이수과점에서 이루어지고 있으며 치수관점에서 극치수문량의 계절성을 평가하는 연구는 미진한 실정이다. 이는 극치수문량을 해석하는 방법론으로서 연최대치계열(annual maxima) 즉, Block Maxima가 이용됨에 따라 나타나는 문제점이다. 그러나 부분기간치계열(partial duration series)을 활용하게 되면 자료의 확충뿐만 아니라 자연적으로 극치수문량의 계절성에 대한 평가 또한 가능하다. 이러한 분석과정을 POT(peak over threshold)분석이라 하며 일정 기준값(threshold) 이상의 자료를 모두 취하여 빈도해석에 이용하는 방법으로서 기존 방법의 경우 연최대값이 일반적으로 7월과 8월에만 존재하게 되지만 POT 분석의 경우 여러 달에 걸쳐 빈도해석을 위한 자료가 구성되게 된다. 이를 빈도해석으로 연계시키기 위해서는 계절성을 비정상성으로 고려하여 모형화 할 수 있는 방법론의 개발이 필요하다. 본 연구에서는 이러한 목적을 위해서 계절성을 고려할 수 있는 비정상성빈도해석 기법의 개념을 제시하고 모형으로 개발하고자 한다. GEV 또는 Gumbel 분포의 매개변수와 계절성을 연계시키기 위해서 Fourier 급수가 활용되며 매개변수는 Bayesian 기법을 통해 최적화 된다. 이를 통하여 설계강수량의 계절적 분포를 정량적으로 해석할 수 있으며 미래의 극치강수량에 대한 분포특성 또한 확률적으로 해석이 가능하다. 본 연구에서 제안된 방법은 국내외 시간강수량자료에 적용되어 적합성과 적용성이 평가된다.

• 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 the Korean earth science society
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• v.27 no.2
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• pp.149-158
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• 2006

This study analyzes the seasonality precipitation using precipitation data from 1973 to 2001 over South Korea. The Seasonality Index and Annual variation of the Seasonality Precipitation were investigated from sixty-three observation stations. The Seasonality Precipitation means the degree of the precipitation falling intensively for some specific months. Spatially, precipitation that has a strong characteristic of regional shower is defined as seasonal precipitation. Precipitation forms are changed with various reasons and mainly the sporadic and local shower precipitation after rain spell in summer. Especially there appears a tendency that this kind of precipitation is sharply increasing in 1990's. Seasonality Index is used as a method to understand seasonal precipitation. If the yearly rainfall is concentrated for some specific months, Seasonality Index is growing gradually. It is confirmed that there is a tendency that all the from sixty-two observation stations Seasonality Index. While Seasonality Index over South of Korea concentrated from June to August because of the summer rain spell in the past ($1973{\sim}1982$), there appears to be a tendency that it concentrated from August and September since the mid 1990's. From the analysis of seasonal precipitation intensity distribution, most of southern Korea is under seasonality precipitation intensity 4. The seasonality precipitation intensity classification results are as follow: most of the observation stations were on a scale intensity of 3 and 4 in the past but currently reads seasonality precipitation intensities of 5 and 6.

• Korean Journal of Environmental Agriculture
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• v.32 no.4
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• pp.294-303
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• 2013

BACKGROUND: It is well known that rice-fields can provide excellent foraging places for birds including seasonal migrants, wintering, and breeding and hence the high biodiversity of rice-fields may be expected. However, how environmental change including climate-changes on life-history and population dynamics in birds on rice-fields has not been fully understood. In order to investigate how climate-change affects population migratory patterns and migration timing, I modeled a population dynamics of birds in rice-fields over a whole year. METHODS AND RESULTS: I applied the Lotka-Volterra equation to model the population dynamics of birds that have been foraging/visiting rice-fields in Korea. The simple model involves the number of interspecific individuals and temperature, and the model parameters are periodic in time as the biological activities related to the migration, wintering and reproduction are seasonal. As results, firstly there was a positive relationship between the variation of seasonal population sizes and temperature change. Secondly, the reduced lengths of season were negatively related to the population size. Overall, the effects of the difference of lengths of season on seasonal population dynamics were higher than the effects of seasonal temperature change. CONCLUSION(S): Climate change can alter population dynamics of birds in rice-fields and hence the variation may affect the fitness, such as reproduction, survival and migration. The unstable balances of population dynamics in birds using paddy rice field as affected by climate change can reduce the population growth and species diversity in rice fields. The results suggest that the agricultural production is partly affected by the unstable balance of population in birds using rice-fields.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2013.11a
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• pp.25-25
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• 2013

식생의 생물계절학적 특성은 지표면과 대기의 에너지와 물 순환에 큰 영향을 미친다. 일반적으로 상층군락과 하층군락의 식생은 미기후의 차이 등에 의해 서로 다른 생물계절학적 특성을 가진다. 이러한 식생의 생물계절학적 특성은 반사도 관측을 통해 추정할 수 있다. 과거부터 원격탐사 기법을 활용하여 식생의 생물계절학적 특성을 추정하는 많은 연구가 수행되어 왔다. 그러나 대부분의 연구는 상층군락과 하층군락의 반사특성을 구분하지 않았다. 본 연구에서는 상층군락과 하층군락 식생의 생물계절학적 특성을 구분하여 탐지하기 위해 red, green, blue 그리고 near-infrared 의 네 가지 파장대를 가진 LED 센서를 이용하였다. LED 센서는 광릉 시험림 내의 활엽수림과 침엽수림 관측지에 서로 다른 네 군데의 높이에 설치되어 각 파장대의 반사도를 산출하였다. 또한 반사도를 이용하여 세 가지 식생지수(NDVI, EVI, Greenness index)를 산출하여 상층식생과 하층식생의 개엽기를 추정하였다.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.233-236
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• 2003

밀양지역 지하수의 계절별 수위변동 특성을 파악하기 위하여 500여개 공을 대상으로 총3회 측정하였다. 봄에서 늦가을까지의 시기별로 측정된 현장지하수위 자료를 이용하여 밀양시 전역의 충적층 및 암반 대수층의 지하수위 등치선도를 작성하였다. 지구통계기법을 이용하여 작성된 밀양지역의 지하수위 등치선도는 계절별로는 큰 변동이 없었으나, 충적대수층과 암반대수층에서의 지하수위 분포양상은 다르게 나타났다. 계절적인 지하수위 변동이 약한 것은 밀양지역의 지하구는 대부분이 농업용수로 이용되고 봄과 가을에는 용수 사용량이 비슷하며, 여름에는 강수량은 맑지만 농업용수의 사용량이 증가하여 함양량이 크지 않기 때문이다. 대수층유형에 따른 지하수위는 암반대수층의 피압 정도와 충적 및 암반 지하수공들의 공간적인 분포 양상에 따라서 영향을 받고 있는 것으로 나타났다.