• Nutritional Sciences
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• 제9권3호
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• pp.201-211
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• 2006

The purpose of this study was to provide dietitians with the guidance in forecasting meal counts for a university/college foodservice facility. The forecasting methods to be analyzed were the following: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winters' methods, and simple linear regression. The accuracy of the forecasting methods was measured using mean squared error and Theil's U-statistic. This study showed how to project meal counts using 10 forecasting methods for dietitians. The results of this study showed that WES was the most accurate forecasting method, followed by $na\ddot{i}ve$ 2 and naive 3 models. However, naive model 2 and 3 were recommended for using by dietitians in university/college dining facilities because of the accuracy and ease of use. In addition, the 2000 spring semester data were better than the 2000 fall semester data to forecast 2001spring semester data.

• 한국국방경영분석학회지
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• 제26권2호
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• pp.39-54
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• 2000

This research is carried out solving problem of reduction in the rate of operation for the k-1 tank in order to increase the availability, caused by the delay in supply of k-1 tank repair parts in field operations. In other words, the study aims to find the most suitable requirement estimate pattern for the main repair parts that are used for k-1 tank. This study intends to present the most suitable requirement estimate pattern for k-1 trank repair pats by comparing the results of repair parts consumption data in relation to their pattern created by the programs of the requirement estimate technique(moving average method) currently used in the Army and adaptive exponential smoothing model. The results of this study numerically proved that the adaptive exponential smoothing model is the most appropriate technique in estimating the requirement for k-1 tank repair parts.

• 대한임베디드공학회논문지
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• 제9권3호
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• Journal of information and communication convergence engineering
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• 제19권4호
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• pp.221-227
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• 2021

An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.

• 한국국방경영분석학회지
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• 제11권1호
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• pp.1-7
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• 1985

A computer-aided simulation model for inventory control was developed using Apple II Plus micro-computer. The model forecasts quarterly demands with Single Exponential Smoothing method and simulates Supply Demand Review and Inventory Level Settings for each items. The simulation is based on the assumption that the demand occurrences have their own probability distributions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Asian Journal of Business Environment
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• 제7권4호
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• pp.35-41
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• 2017

Purpose - This paper aims to examine several time series models to predict sales of department stores and discount store markets in South Korea, while other previous trial has performed sales of convenience stores and supermarkets. In addition, optimal predicted values on the underlying model can be got and be applied to distribution industry. Research design, data, and methodology - Two retailing types, under investigation, are homogeneous and comparable in size based on 86 realizations sampled from January 2010 to February in 2017. To accomplish the purpose of this research, both ARIMA model and exponential smoothing methods are, simultaneously, utilized. Furthermore, model-fit measures may be exploited as important tools of the optimal model-building. Results - By applying Holt-Winters' additive seasonality method to sales of two large-scale retailing types, persisting increasing trend and fluctuation around the constant level with seasonal pattern, respectively, will be predicted from May in 2017 to February in 2018. Conclusions - Considering 2017-2018 forecasts for sales of two large-scale retailing types, it is important to predict future sales magnitude and to produce the useful information for reforming financial conditions and related policies, so that the impacts of any marketing or management scheme can be compared against the do-nothing scenario.

• Journal of the Korean Data and Information Science Society
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• 제22권6호
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• pp.1167-1173
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• 2011

본 연구의 목적은 기초자치단체인 시 군의 장래 학생수 추계를 위한 알고리즘을 제시하는데 있다. 인구추계를 위하여 시계열 방법의 이중지수평활법을 사용하였다. 결론적으로 2044년까지 고등학교 3학년 학생수의 경우에는 칠곡, 구미, 경산, 안동, 포항, 김천은 40%-70% 정도 줄고, 나머지 시 군은 70%-95%이상 학생이 줄며, 심지어 군위, 청송, 성주, 울릉, 의성, 영양 등 6개 군은 26년 후인 2036년에는 고등3학년 학생이 없을 것으로 추정된다. 초등1학년의 경우는 2044년까지 칠곡, 구미, 안동, 경산까지는 약 50%-70% 사이까지 줄어들고, 나머지 시 군은 70%-100% 줄어든다. 특히 군위, 의성, 성주, 청송, 청도, 울진, 고령은 2044년까지 순차적으로 초등1학년 학생이 없을 것으로 추정된다.

• 정보처리학회논문지A
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• 제10A권3호
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• pp.261-268
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• 2003

본 논문에서는 파일이 요청된 순간에는 파일의 선호도만을 조사하고, 일정 시간이 흐른 후에 선호도가 높을 것으로 예상되는 파일들을 일관적으로 캐싱하는 새로운 캐싱 전략을 소개한다. 예측된 선호도 기반 게으른 캐싱 전략(Forecasted Popularity Based Lazy Caching Strategy)은 웹 파일의 선호도를 지수평활법을 사용하여 예측하여 기존의 캐싱 전략보다 높은 캐시 적중률을 보여준다. 국내외 5개 웹 서버로부터 수집한 로그 파일을 대상으로 실험한 결과에 의하면, 선호도의 예측이 정확할수록 높은 캐시 적중률을 나타낸다. 이는 엘 파일의 선호도 예측 기법에 대한 연구를 통해 캐시의 성능을 향상시킬 수 있음을 보여준다.

• 한국정보과학회논문지:정보통신
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• 제29권5호
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• pp.495-500
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• 2002

We propose a new estimation method for rate adjustment in the face of a packet loss in the TFRC protocol, a TCP-Friendly congestion control protocol for UDP flows. Previous methods respond in a sensitive way to a single packet loss, resulting in oscillatory transmission behavior. This is an undesirable for multimedia services demanding constant bandwidth. The proposed TFRC provides more smooth and fair (against TCP flows) transmission through collective response based on multiple packets loss events. We show our "Exponential smoothing method" performs better than known "Weight smoothing method" in terms of smoothness and fairness.