• Journal of The Korean Association For Science Education
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• v.29 no.7
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• pp.759-766
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• 2009

This study was conducted to examine 10 Belizean teachers' conceptions about the causes of seasonal change. This research was conducted with an integrated method using a open ended written test and an interview which included a drawing. There are four categories, explained by the teachers, as the causes of seasonal changes. They are; climate, rotation of the earth on its axis, revolution of the earth around the sun, and the tilting of earth's axis as it revolves. Most teachers misunderstood that the first of three categories was responsible for seasonal change. Second, it is more effective to use the integrated method shown in this research than to use only a written test when seriously investigating the causes and understanding of seasonal change. Third, 8 out of 10 teachers could not correctly explain the causes of seasonal change. The reasons for seasonal change seemed to be hard for the informants to understand even though it was taught in elementary, middle, high school, and college elective classes.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.3
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• pp.203-212
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• 2009

The bullwhip effect refers to the phenomenon where demand variability is amplified when one moves upward a supply chain. In this paper, we exactly quantify the bullwhip effect for cases of seasonal demand processes in a two-echelon supply chain with a single retailer and a single supplier. In most of the previous research, some measures of performance for the bullwhip effect are developed for cases of non-seasonal demand processes. The retailer performs demand forecast with a multiplicative seasonal mixed model by using the minimum mean square error forecasting technique and employs a base stock policy. With the developed bullwhip effect measure, we investigate the impact of seasonal factor on the bullwhip effect. Then, we prove that seasonal factor plays an important role on the occurrence of the bullwhip effect.

• Journal of the korean society of oceanography
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• v.38 no.3
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• pp.111-121
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• 2003

The seasonal succession of zooplankton communities in the coastal area off Incheon, Kyeonggi Bay, was investigated with the samples collected at intervals of 10 to 15 days from January 1999 to December 2000. Total abundance of zooplankton communities showed remarkable seasonal variations, ranged from 1,100 to $120,400{\;}indiv./\textrm{m}^3$, and annual mean abundance was $22,000{\;}indiv./\textrm{m}^3$. There were several times of the total abundance during a year, and the timing ofhigh abundances were about the same in 1999 and 2000. During the study period except summer, the abundance of dinoflagellate Noctiluca scintillans and copepod Acartia hongi contributed to the most part of total zooplankton. Whereas, during summer, smaller copepod Oithona davisae and Paracalanus crassirostris were dominant species. Zooplankton communities in the coastal waters off Incheon showed typical characteristics of coastal-estuarine communities, which were dominated by a few species, and abrupt seasonal variations in abundance. We suggest that the seasonal succession and abundance variations of zooplankton communities were caused by the seasonal variations in water temperature and by the seasonally varying phytoplankton biomass in the study area.

• 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.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.335-348
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• 2009

Fuzzy regression is used as a complement or an alternative to represent the relation between variables among the forecasting models especially when the data is insufficient to evaluate the relation. Such phenomenon often occurs in seasonal time series data which require large amount of data to describe the underlying pattern. Semiparametric model is useful tool in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. In this paper we propose fuzzy semiparametric support vector regression so that it can provide good performance on forecasting of the seasonal time series by incorporating into fuzzy support vector regression the basis functions which indicate the seasonal variation of time series. In order to indicate the performance of this method, we present two examples of predicting the seasonal time series. Experimental results show that the proposed method is very attractive for the seasonal time series in fuzzy environments.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.68-75
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• 1991

The purpose of this thesis is to develop the new technique for the analysis of seasonal time series by extending the vector sample auto-correlation function(VSACF), which was developed for ARMA modelling procedure. After the problems of VSACF for modelling seasonal time series are investigated, the adjacent variance is defined and used for decomposing the seasonal factor from the seasonal time series. The seasonal indices are calculated and the VSACF is applied to the transformed series. The automatic procedure for modelling seasonal time series is suggested and applied to the real data, the international airline passenger travel.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.9-22
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• 2008

As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.

• 한국해양학회지
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• v.2 no.1_2
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• pp.13-23
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• 1967

Seasonal changes in the primary production of surface water in Suyong Bay, Pusan, were measured using a light-dark bottle method. Gross photosynthesis followed a distinct seasonal change with highest levels in spring and fall. Respiration of plankton community showed its maximum only in the late summer and early fall. Net photosynthesis of plankton community is considerably variable throughout year, but followed a seasonal change similar to gross photosynthesis. Seasonal changes in temperature and salinity are related to the seasonal change in plankton metabolism.

• Journal of the Korean Statistical Society
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• v.22 no.1
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• pp.125-132
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• 1993

Large sample test of homogeneity for a panel of more than two seasonal autoregressive processes is derived and its limiting distribution is found. Detailed results are shown for the important special case that the seasonal and nonseasonal autoregressive components are both of order one.

• 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.