• Korean Business Review
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• v.4
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• pp.281-295
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• 1991

Nothing is more incorrect than forecasting. Nevertheless, forecasting is one of the most important business activities for the effective management. There has been rapid changes of the growth rate in every respect of the Korean hospitaity industry, especially the hotel industry, before and after the 88 Olympic Games. Therefore, the hoteliers shall be in need of more-than-ever accourate demand forecasting for the more systematic management and control. Under the above circumstances, this study suggested the best forecasting technique and method for the better sales and operations of the hotel rooms. The number of rooms sold is selected as a dependent variable of this study which is regarded as the best representative factor of measuring the growth rate of the rooms division performance of the hotels. The first step was to select the most verifiable independent variable diferently from the other countries or other areas of Korea. As a result, the number of foreign visitors was chosen. Empirical research, i.e. correlation and multiple regression analysis, shows that this independent variable has a strong relationship with the dependent variable told above. The second procedure was to estimate the number of rooms will be sold in 1991 on the basis of the formula calculated through the multiple regression analysis. Time series technique was conducted using the data of the number of foreign visitors by purpose of travel from 1987 to 1990. For the more correct forecasting, however, it would be desirable to adopt the data from 1989 considering the product or the industry life cycle. In addition, deeper analysis for the monthly or seasonal forecasting method is needed as a future research.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1235-1243
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• 2015

Increased data volume in the ICT area has increased the importance of forecasting accuracy for internet traffic. Forecasting results may have paper plans for traffic management and control. In this paper, we propose combined forecasts based on several time series models such as Seasonal ARIMA and Taylor's adjusted Holt-Winters and Fractional ARIMA(FARIMA). In combined forecasting methods, we use simple-combined method, MSE based method (Armstrong, 2001), Ordinary Least Squares (OLS) method and Equality Restricted Least Squares (ERLS) method. The results show that the Seasonal ARIMA model outperforms in 3 hours ahead forecasts and that combined forecasts outperform in longer periods.

• Journal of Fashion Business
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• v.15 no.6
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• pp.176-203
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• 2011

In today's intensifying global competition, Korean fashion industry is relying on only qualitative data for feasibility study of future projects and developmental plan. This study was conducted in order to support establishment of a scientific and rational management system that reflects market demand. First, fashion market size was limited to the total amount of expenditure for fashion clothing products directly purchased by Koreans for wear during 6 months in spring and summer and 6 months in autumn and winter. Fashion market forecasting model was developed using statistical forecasting method proposed by previous research. Specifically, time series model was selected, which is a verified statistical forecasting method that can predict future demand when data from the past is available. The time series for empirical analysis was fashion market sizes for 8 segmented markets at 22 time points, obtained twice each year by the author from 1998 to 2008. Targets of the demand forecasting model were 21 research models: total of 7 markets (excluding outerwear market which is sensitive to seasonal index), including 6 segmented markets (men's formal wear, women's formal wear, casual wear, sportswear, underwear, and children's wear) and the total market, and these markets were divided in time into the first half, the second half, and the whole year. To develop demand forecasting model, time series of the 21 research targets were used to develop univariate time series models using 9 types of exponential smoothing methods. The forecasting models predicted the demands in most fashion markets to grow, but demand for women's formal wear market was forecasted to decrease. Decrease in demand for women's formal wear market has been pronounced since 2002 when casualization of fashion market intensified, and this trend was analyzed to continue affecting the demand in the future.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.658-663
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.148-150
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• 2005

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. This paper presents a methodology of a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) based on the Time Series. And also we suggested a correction algorithm to minimize the forecasting error in order to improve efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using Historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1657-1660
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• 2013

Short-term load forecasting for Chusok and New Year's consecutive holidays is very difficult, due to the irregular characteristics compared with ordinary weekdays and insufficient holidays historical data. During consecutive holidays of New Year and Chusok, most of industries reduce their operation rates and their electrical load levels. The correlation between businesses' operation rates and their loads during consecutive holidays of New Year and Chusok is analysed and short-term load forecasting algorithm for consecutive holidays considering businesses' operation rates of industries is proposed. Test results show that the proposed method improves the accuracy of short-term load forecasting over fuzzy linear regression method.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.44-50
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• 2008

Focusing on the exponential smoothing method equivalent to (1, 1) order ARMA model equation, a new method of estimating smoothing constant using exponential smoothing method is proposed. This study goes beyond the usual method of arbitrarily selecting a smoothing constant. First, an estimation of the ARMA model parameter was made and then, the smoothing constants. The empirical example shows that the theoretical solution satisfies minimum variance of forecasting error. The new method was also applied to the stock market price of electrical machinery industry (6 major companies in Japan) and forecasting was accomplished. Comparing the results of the two methods, the new method appears to be better than the ARIMA model. The result of the new method is apparently good in 4 company data and is nearly the same in 2 company data. The example provided shows that the new method is much simpler to handle than ARIMA model. Therefore, the proposed method would be better in these general cases. The effectiveness of this method should be examined in various cases.

• IE interfaces
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• v.16 no.4
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• pp.400-410
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• 2003

Most of the demand forecasting which have been studied is about long-term time series over 15 years demand forecasting. In this paper, we set up the most optimal ARIMA model for the short-term time series demand forecasting and suggest demand forecasting system for short-term time series by appraising suitability and predictability. We are going to use the univariate ARIMA model in parallel with the bivariate transfer function model to improve the accuracy of forecasting. We also analyze the effect of advertisement cost, scale of branch stores, and number of clerk on the establishment of marketing policy by applying statistical methods. After then we are going to show you customer's needs, which are number of buying products. We have applied this method to forecast the annual sales of refrigerator in four branch stores of A company.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1186-1191
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• 2014

Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.380-389
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• 2000

This paper presents a method for the regional long-term load forecasting in metropolitan area considering econimic indicator with the assumption that energy demands propoprtionally increases under the economic indicators. For the accurate load forecasting, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because load forecasting results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. Three steps for the regional long-term load forecasting are microscopically and macroscopically used for the regional long -term load forecasting in order to increase the accuracy and practicality of the results.