• Journal of applied mathematics & informatics
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• v.4 no.1
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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.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.59-74
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• 2016

Electricity sales are directly measured from individual consumers, which could minimize the time gap between data collection and public announcement. Furthermore, industrial electricity sales are highly linked with production and output. Therefore, industrial electricity consumption can be used to track production and output in real time. By using the high-frequency data of industrial electricity sales, this study develops the daily electricity business index (DEBI) to capture the daily economic status. The steps used to formulate DEBI are as follows: (1)selection of the explanatory variables and period, (2) amendment of the seasonal adjustment to eliminate daily temperature and effective day effects, (3) estimation of the weighted value via variables by using PCA, (4) calculation of DEBI and commencement of validation tests. Our empirical analysis and the Hodrick-Prescott filter analysis show that DEBI is highly related to existing economic indices.

• Development and Reproduction
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• v.16 no.1
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• pp.1-8
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• 2012

The reproduction of animals is a way to maintain their species and demands a large amount of energy. The golden hamsters are seasonal breeders whose reproductive activities are regulated by photoperiod (length of day time in a day). The photic information received is transported to the pineal gland via many steps. Melatonin produced by the pineal gland affects the reproductive neuroendocrine system to manage reproductive activities. The major regulator neurons, secreting gonadotropin-releasing hormone, integrate all kinds of information to govern the reproductive frame hypothalamuspituitary-gonad axis. The elements impinging on the neurons are recently outspread. Thus the present review is to briefly survey the elements discovered newly and subjected to the active research realm and their correlations, focusing on the regulation of reproduction in mainly male golden hamsters as a representative animal.

• The Korean Journal of Ecology
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• v.28 no.6
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• pp.353-363
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• 2005

We developed a local-scale ecophysiological model, Sim-CYCLE Fine by modifying Sim-CYCLE which was developed for a global scale simulation. Sim-CYCLE fine is able to simulate not only carbon fluxes but also plant growth with various time-steps from an hour to a month. The model outputs of $CO_2$ flux and biomass/LAI were highly reliable; we validated the model results with measurements from the eddy covariance technique and the harvest method ($R^2$ values of around 0.9 for both). The results suggested that the phonology and the seasonal dynamics of the $C_3/C4$ plant communities affected significantly the carbon fluxes and the plant growth during the plant growing season.

• Journal of the Korean Geographical Society
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• v.46 no.1
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• pp.20-35
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• 2011

This study suggested the systematic steps for quality control, construction of the climatological reference series and homogeneity test and adjustment of temperature series in the Republic of Korea. It also attempted to evaluate more accurate magnitude of change using adjusted temperature data. All erroneous values produced by quality control were detected by internal inconsistency check. The method selected for homogeneity test in this study well defined fairly correct signals of station relocations. Therefore, this method might be regarded as the appropriate one to test homogeneity of temperature series of the Republic of Korea. The increase of temperature of the Republic of Korea after the adjustment were bigger than before the adjustment of annual and seasonal mean temperature. Adjusted temperature data produced by these steps will enable to evaluate more accurate characteristics and magnitude of climate change.

• Journal of the Korean earth science society
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• v.39 no.3
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• pp.260-274
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• 2018

The purpose of this study was to propose curriculum and teaching sequence for seasonal change by exploring a learning progression. For the purpose, 4 steps of construct modeling approach (specifying construct, item design, outcome space, and measurement model) proposed by Wilson (2005) was applied. In the stage of specifying construct, 'length of shadow according to seasons', 'position of constellation according to seasons', 'seasons of the southern hemisphere and northern hemisphere', 'cause and phenomenon of seasonal change' were selected as the subconstructs of seasonal changes, and constructed a construct map showing the level of development from level 1 to level 4 for each subconstruct based on the results of the previous research. In the item design stage, we developed five assessment items consisting of 3 items in the form of C-E (choose and explain) and two items in the form of CR (constructed response), applied it to 383 elementary, middle and high school students. In the outcome space stage, the students' responses to the assessment items were categorized based on the construct map. The categories were classified into 4 levels according to student ability and scores of 1-4 were given. In the measurement model stage, we applied the partial credit model of the Rasch model and compared whether the learning pathway created from the results of students' response coincides with the construct map. Based on the results of the research, we modified the construct map and finally created hypothetical learning progression on seasonal change. Finally, we proposed an orientation of curriculum amendment and effective teaching sequence for seasonal change.

• Journal of the Korean earth science society
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• v.34 no.4
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• pp.356-367
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• 2013

We study on the concept and reason of seasonal change that 164 university students have. Subsequently the concept types on the seasonal change are classified according to the characteristics and conceptual change after teaching on astronomy. All of the students were simply checked by the questionnaire of multiple choice and essay method before learning on the subjects. And then they answered to questionnaires of similar type after one semester. By the analyzed results, we classify it to three steps of hierarchical concept structure. The first step is the cosmic perspective that is related to the Earth's condition and motion. The second step is the influence of the Earth that is directly affected by the first step. The third step is observer's perspective on the Earth depending on the second step. Among the answers, the first step is prominent and second step is rare. The answers on the reason of seasonal change show some kinds of type which are 1st, 1-2nd, 1-3rd, and 1-2-3rd step. By the result, it is arranged in sequence like as 1-3rd>1st>1-2nd>1-2-3rd type. The lowest number of students was 2nd step of the Sun's altitude and duration of daytime in pre-test. However the students of 2nd step obtained more correct scientific concept on the seasonal change after learning on the subjects, and got the higher score in the post-test than in the pre-test. We found how much important the hierarchical structure on the reason of seasonal change is. As the results, second step on the learning of the Sun's altitude and duration of daytime essentially have to teach after first step. And then third step have to teach. At last, it is sure that the students can obtain the concept of seasonal change.

• Biotechnology and Bioprocess Engineering:BBE
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• v.8 no.1
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• pp.23-31
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• 2003

A mathematical model was formulated to simulate the long-term performance of an anaerobic bioreactor designed to digest Korean food wastes. The system variables of various decomposition steps were built into the model, which predicts the temporal characters of Solid waste, and volatile fatty acid (VFA) in the reactor, and gas production in response to various input loadings and temperatures. The predicted values of VFA and gas production were found to be in good agreement with experimental observations in batch and repeated-input systems. Finally, long-term reactor performance was simulated with respect to the seasonal temperature changes from 5C in winter to 25C in Summer at different food waste input loadings. The simulation results provided us with information concerning the success or failure of a process during long-term operation .

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.143-154
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• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• Atmosphere
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• v.19 no.3
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• pp.269-287
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• 2009

CAT (clear-air turbulence) forecasting algorithm, the Graphical Turbulence Guidance (GTG) system developed at NCAR (national center for atmospheric research), is evaluated with available observations (e.g., pilot reports; PIREPs) reported in South Korea during the recent 5 years (2003-2008, excluding 2005). The GTG system includes several steps. First, 44 CAT indices are calculated in the domain of the Regional Data Assimilation and Prediction System (RDAPS) analysis data with 30 km horizontal grid spacing provided by KMA (Korean Meteorological Administration). Second, 10 indices that performed ten best forecasting scores are selected. Finally, 10 indices are combined by measuring the score based on the probability of detection, which is calculated using PIREPs exclusively of moderate-or-greater intensity. In order to investigate the best performance of the GTG system in Korea, various statistical examinations and sensitivity tests of the GTG system are performed by yearly and seasonally classified PIREPs. Performances of the GTG system based on yearly distributed PIREPs have annual variations because the compositions of indices are different from each year. Seasonal forecasting is generally better than yearly forecasting, because selected CAT indices in each season represent meteorological condition much more properly than applying the selected CAT indices to all seasons. Wintertime forecasting is the best among the four seasonal forecastings. This is likely due to that the GTG system consists of many CAT indices related to the jet stream, and turbulence associated with the jet stream can be activated mostly in wintertime under strong jet magnitude. On the other hand, summertime forecasting skill is much less than other seasons. Compared with current operational CAT prediction system (KITFA; Korean Integrated Turbulence Forecasting System), overall performance of the GTG system is better when CAT indices are selected seasonally.