• Animal cells and systems
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• 제2권2호
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• pp.165-176
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• 1998

Seasonal changes in microzooplankton abundance were identified in the mesohaline Chesapeake Bay and several tributaries from July 1992 through December 1995. Ciliates numerically dominated, comprising over 90% of the total microzooplankton density and aloricate ciliates usually outnumbered loricate ciliates. Copepod nauplii accounted for the highest microzooplankton biomass (>75% in dry weight). Rotifers made small contributions to the total microzooplankton density and biomass (<5%). Time series analysis indicated a twelve month cycle in microzooplankton abundance, and mid-summer(August) peaks for copepod nauplii, and a spring through fall peaks (May-October) for ciliates. Rotifers showed two seasonal peaks: one in mid-summer(August) at the river stations and the other in mid-winter(February) at the mesohaline stations. Seasonal peaks of copepod nauplii and rotifers coincided with the mesozooplankton abundance peak. On the other hand, ciliate maximum usually occurred between the phytoplankton and mesozooplankton peaks. This pattern of microzooplankton seasonality suggests the intermediate trophic role of microzooplankton (especially ciliates) between the phytoplankton(especially picophytoplankton) and mesozooplankton in Chesapeake Bay and its tributaries.

• 한국멀티미디어학회논문지
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• 제25권1호
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• Journal of the Korean Data and Information Science Society
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• 제12권1호
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• pp.71-78
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• 2001

Three test statistics are proposed for the presence of seasonality in multiplicative seasonal time series models. Further their common limiting distribution is derived under some assumptions.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 춘계 학술발표회 논문집
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• pp.13-15
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• 2005

In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.

• Journal of applied mathematics & informatics
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• 제4권1호
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• pp.135-135
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• 1997

Times series modeling plays an important role in the field of engineering, Statistics, Biomedicine etc. Model identification is one of crucial steps in the modeling of an AutoRegreesive Moving Average(ARMA(p, q)) process for real world problems. Many techniques have been developed in the literature (Salas et al., McLeod et al. etc.) for the identification of an ARMA(p, q) Model. In this paper, a new technique called The Generalised Parameters Technique is formulated for seasonal and non-seasonal ARMA model identification. This technique is very simple and can e applied to any given time series. Initial estimates of the AR parameters of the ARMA model are also obtained by this method. This model identification technique is validated through many theoretical and simulated examples.

• 한국ITS학회 논문지
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• 제20권6호
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• pp.1-13
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• 2021

본 연구에서는 도로교통분야의 계획, 설계, 유지관리, 연구 등 다양한 목적으로 활용되고 있는 교통량 데이터의 정확도 확보를 위해 시계열 분석 기법을 적용하여 교통량 데이터의 보정 및 예측을 수행하였다. 기존 알고리즘의 경우 주기성 및 계절성이 강하거나 불규칙한 데이터에 한계를 보이고 있어 교통량 데이터와 같은 자료에 적용하기에는 한계가 있다. 이러한 한계점을 극복하고 보완하기 위해 ARIMA 모형에 자기상관 모형인 SAR(Seasonal Auto Regressive)과 계절 이동평균 모형인 SMA(Seasonal Moving Average)가 결합된 분석 기법인 SARIMA 모형을 적용하였다. 분석결과 최적 파라미터 조합인 SARIMA(4,1,3)(4,0,3) 12 모형을 활용한 교통량 예측 결과 평균 85% 정도의 우수한 성능을 보였다. 본 연구를 통해서 교통량 데이터의 결측 발생 시 교통량 보정 및 예측의 정확도를 높일 수 있으며, 교통량 데이터 외에도 계절성에 영향을 받는 시계열 데이터에 적용이 가능하다.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.557-573
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• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.

• 응용통계연구
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• 제29권4호
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• pp.581-594
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• 2016

본 논문은 이노베이션 상태공간모형을 근간으로 기존의 지수평활법을 포괄할 수 있는 다중 계절형 모형을 소개한다. 특히 이 모형은, 기존 모형의 한계를 극복하고 동일한 계절 내의 다양성을 표현할 수 있도록 계절 성분을 행렬로 표현하는 정교한 구조를 가지고 있다. 이런 구조를 이용하면 비슷한 패턴을 가지는 계절 성분의 모수를 그룹별로 분류할 수 있다. 따라서, 다중 계절형 모형은 모수절약 원칙을 달성할 수 있으며 모형의 해석이 용이한 장점을 가지고 있을 뿐만 아니라, 잠재적으로 임의의 개수의 계절성도 수용 가능하다. 본 연구에서는 다중 계절형 모형을 이용하여 시간 단위로 관측된 한국 전력 수요량을 분석하고 예측한다. 특히, 시간별 전력 수요량의 계절성은 1일 및 1주일의 두 가지로 고려되었고 이를 토대로 유사한 요일들은 공통 계절로 그룹화하였다. 모형의 예측 성능을 평가하기 위하여 기존 지수평활법의 예측 결과와 비교하였다. 그 결과, 다중 계절형 모형이 기존 지수평활법보다 예측력이 우수함을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제29권3호
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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.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.1308-1314
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• 2008

In this paper, Seoulmetro that is the first operation organization which operates a city railroad rolling-stock maintenance RIMS(rolling stock information maintenance system) collected and analyzed a light maintenance data and introduced time a series analysis technique to find the way how to contribute to a use efficiency improvement of a city railroad. The purpose of time a series analysis is to remove a seasonal change including data and to check an irregular fluctuation. First of all, a collection range of the data comes under a light maintenance, however it needs a data of more than 3 years to check the seasonal change. We put a study for an accumulated scope that the data satisfy a period like this and are able to extend a range of the study when time flys forward. The data used for study is filtered using a movement average method after passing proper selection working and is solved with a method which looks for season index. Using the season index that was getten in here, we predict a light working frequency, if it has an irregular change, we will contribute it to a city railroad a use efficiency improvement and establish the cause by carrying out prevent maintenance in advance.