• 수도
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• s.40
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• pp.32-41
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• 1987

• Proceedings of the KOR-KST Conference
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• 1996.06a
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• pp.175-197
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• 1996

• Transportation Technology and Policy
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• v.10 no.4
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• pp.54-60
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• 2013

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.149-150
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• 2002

• Proceedings of the Korea Technology Innovation Society Conference
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• 1999.11c
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• pp.47-64
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• 1999

• 수도
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• s.30
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• pp.24-48
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• 1984

• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.1
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• pp.50-56
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• 2014

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

• Journal of Korean Society of Forest Science
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• v.97 no.4
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• pp.458-466
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• 2008

This study was carried out to forecast long-term supply and demand of chestnut and to analyze the impacts of change in the environment of domestic and international chestnut markets. For these ends, the study developed a partial equilibrium market model, in which in-shelled chestnut market was vertically linked to shelled chestnut market. To examine the predictive ability of the model for the endogenous variables ex-post simulation was run for the period 1990 through 2003. In general, all endogenous variables reproduced the historical trends during the period except for disuse areas and newly established areas. The results of forecasting supply and demand show that domestic in-shelled chestnut production is estimated to decrease slightly from 76,447 ton in 2005 to 76,286 ton in 2020 and that exports of shelled chestnut continue to be decreased.

• Journal of the military operations research society of Korea
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• v.34 no.2
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• pp.79-101
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• 2008

This study is for improvement of repair part demand forecasting method of Republic of Korea Air Force aircraft. Recently, demand prediction methods are Weighted moving average, Linear moving average, Trend analysis, Simple exponential smoothing, Linear exponential smoothing. But these use fixed weight and moving average range. Also, NORS(Not Operationally Ready upply) is increasing. Recommended method of Box-Jenkins' ARIMA can solve problems of these method and improve estimate accuracy. To compare recent prediction method and ARIMA that use mean squared error(MSE) is reacted sensitively in change of error. ARIMA has high accuracy than existing forecasting method. If apply this method of study in other several Items, can prove demand forecast Capability.