• 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.

• Ecology and Resilient Infrastructure
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• v.5 no.3
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• pp.125-133
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• 2018

Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.

• Journal of the Korean Society of Food Culture
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• v.26 no.2
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• pp.170-177
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• 2011

We examined household's food expenditures in this study. The empirical work outlined here used quarterly data from 2003 Q1 to 2010 Q3. All variables are in log form and were obtained from the Korea National Statistical Office. The food items included cereals, dairy products, fruits, meat, vegetables, and alcoholic beverages. We applied the ordinary least squares method to a model consisting of household income and seasonal dummies. This is because household expenditures are ordinarily a function of income and have seasonal characteristics. The household's food consumption patterns also reflect the prevailing social and environmental circumstances. This study showed that the income coefficients of cereals, meat, dairy products, and alcoholic beverages tend to increase in the long-run, whereas those of vegetables and fruits decreased. The results also revealed that consumption of alcoholic beverages and meat was greatly affected by household income fluctuations, whereas those of vegetables and dairy products were not sensitive to income. The impulse response functions indicated that expenditures not only increased slowly before peaking one to eight quarters after the income shock but declined very slowly to pre-shock levels. The response of dairy products at the twelfth step was three times as large as that of the first step.

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