• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.615-624
• /
• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• Journal of Korean Society of Transportation
• /
• v.32 no.6
• /
• pp.600-614
• /
• 2014

This paper develops a model to forecast container volumes of all Korean seaports using a Seasonal ARIMA (Autoregressive Integrated Moving Average) technique with the quarterly data from the year of 1994 to 2010. In order to verify forecasting accuracy of the SARIMA model, this paper compares the predicted volumes resulted from the SARIMA model with the actual volumes. Also, the forecasted volumes of the SARIMA model is compared to those of an ARIMA model to demonstrate the superiority as a forecasting model. The results showed the SARIMA Model has a high level of forecasting accuracy and is superior to the ARIMA model in terms of estimation accuracy. Most of the previous research regarding the container-volume forecasting of seaports have been focussed on long-term forecasting with mainly monthly and yearly volume data. Therefore, this paper suggests a new methodology that forecasts shot-term demand with quarterly container volumes and demonstrates the superiority of the SARIMA model as a forecasting methodology.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.5
• /
• pp.27-39
• /
• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.25-30
• /
• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of Korean Society of Transportation
• /
• v.31 no.3
• /
• pp.3-18
• /
• 2013

This study develops a forecasting method to estimate air cargo demand from ICN(Incheon International Airport) to all airports in EU with Seasonal Autoregressive Integrated Moving Average (SARIMA) Model using volumes from the first quarter of 2000 to the fourth quarter of 2009. This paper shows the superiority of SARIMA Model by comparing the forecasting accuracy of SARIMA with that of other ARIMA (Autoregressive Integrated Moving Average) models. Given that very few papers and researches focuses on air route, this paper will be helpful to researchers concerned with air cargo.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
• /
• v.3 no.3
• /
• pp.189-196
• /
• 1999

The community structure, phytomass, and primary productivity in Thuja orientalis stands on a limestone area located in Maepo-up, Chungbuk province in Korea were estimated quantitatively. Seven species including a small proportion of Quercus dentata were identified in the tree layer, 26 species including Ulmus macrocarpa in the shrub layer, and 79 species including Carex lnceolata in the herb layer of the Thuja stands. The vertical distribution of the fine root phytomass exhibited a power functional decrease relative to the soil depth. The seasonal changes in the fine root phytomass at a soil depth of 5 cm were closely related to the pecipitation in the study area. The productivity of the stand of stems, branches, leaves, and roots were 10.72, 0.82, 0.45 and 6.46 ton DM. $ha^{-1}$ .$yr^{-1}$, respectively. The Thuja stand had a high foliage(25%) and low rate of production per unit of foliage. The annual turnover rate of the fine roots int he Thuja stand was 6.71 $yr^{-1}$. The net primary production of the overstory including the understory was estimated at 19.48 ton DM.$ha^{-1}$.$yr^{-1}$ including an underground section of 6.46 ton DM.$ha^{-1}$.$yr^{-1}$(33%). The allocation ratio of net production to root was lower in the limestone Thuja communities than at the nearby non-limestone ones, whereas the production efficiency to leaf weight was higher in the limestone communities. These results would seem to indicate that the limited production capacity is due to the calcium toxicity and low availability of iron and phosphorus in a limestone soil with a high pH, calcium, and bicarbonate content with a strategy for survival in a hostile habitat.

• KDI Journal of Economic Policy
• /
• v.14 no.1
• /
• pp.121-145
• /
• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of Fisheries and Marine Sciences Education
• /
• v.28 no.1
• /
• pp.211-220
• /
• 2016

Species composition of aquatic organism in the marine ranching area of Gunsan, Korea were investigated using shrimp beam trawl from May to December in 2010. A total of 91 species, $98,127ind./km^2$ and $877.6kg/km^2$ of aquatic organism were collected. Among them, species were included 60 species in Pisces, 21 in Crustacea and 10 in Mollusca. The individual dominant species, occupying over 10% of total individuals, were Latreutes anoplonyx($47,327ind/km^2$, 48.23%), Crangon hakodatei($11,578ind./km^2$, 11.80%) and Trachysalambria curvirostris($10,237ind./km^2$, 10.40%). And the biomass dominant species, occupying over 9% of total biomass, were Paralichthys olivaceus($135kg/km^2$, 15.4%), Okamejei kenojei($98.2kg/km^2$, 11.2%) and Portunus tribuberculatus($84.8kg/km^2$, 9.6%). From the cluster and MDS analysis based on Bray-Curtis similarity matrix of fourth root transformed data of number of species and individuals per unit area collected more than two times during this survey by each month and station was divided into three different groups. Group A showed seasonal similarity of characteristic of distribution in August and November, Group B in December and Group C in May.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.8 no.2
• /
• pp.147-154
• /
• 1988

Korea has the ground which freezes in winter and melts in warmer seasons by turns. Therefore, in designing civil-structures or buildings on such ground, the depth of seasonal frost penetratio must be considered. In this paper, approximate contours of the maximum depth of frost penetration in Korea is presented. It was found that the maximum depth of frost penetration did not have the linear relationship to square root of the freezing index. In order to establish more reliable method to estimate the maximum depth of frost penetration, a new empirical equation is introduced. In the presented equation, the dry unit weight and water content of soil are considered in addition to the freezing index. And the equation is compared with other previous equations used so far.

• International Area Studies Review
• /
• v.13 no.2
• /
• pp.277-297
• /
• 2009

This paper empirically investigates a long-run and short-run equilibrium relationships for exchange rate and stock prices in Korea and Japan from January 1998 to July 2008. Because using monthly data in my study, analyzes unit root test and VEC model including seasonality to overcome bias that happen in seasonal adjustment. The empirical evidence suggests that exists strong evidence supporting the long-run cointegration relationships between exchange rates and stock prices of the Korea and Japan. This implies that it is possible to predict one market from another for both countries, which seems to violate the efficient market hypothesis. In the long-run a negative relationship running from the KRW/JPY real exchange rate to the stock prices of Korea strongly argues for the traditional approach.