• Nutritional Sciences
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• v.9 no.3
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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.

• Journal of the military operations research society of Korea
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• v.26 no.2
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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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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• v.19 no.4
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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.

• Journal of the military operations research society of Korea
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• v.11 no.1
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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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• v.11 no.10
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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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• v.7 no.4
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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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• v.22 no.6
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• pp.1167-1173
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• 2011

The goal of this paper is to suggest an algorithm to get forecasting for the numbers of students in the city or county in local government by using the double exponential smoothing method. By 2044 year, the third year of high school students in the Chilgok, Gumi, Gyeongsan, Andong, Pohang and Gimchen are reduced about 40-70%, the those of in the remaining city or county are reduced about 70-95%. In conclusion, the forecasting numbers of students of the 23 counties in Kyungbuk Province are on the decrease to 40%-100% until 2044 year in comparison with the numbers of students on 2010 years.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.261-268
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• 2003

In this paper, we propose a new caching strategy for web servers. The proposed strategy collects only the statistics of the requested file, for example the popularity, when a request arrives. At a point of time, only files with higher forecasted popularity are cached all together. Forecasted popularity based lazy caching (FPLC) strategy uses exponential smoothing method for forecast popularity of web files. And, FPLC strategy shows that the cache hit ratio and the cache transfer ratio are better than those produced by other caching strategy. Furthermore, the experiment that is performed with real log files built from web servers shows our study on forecast method for popularity of web files improves cache efficiency.

• Journal of KIISE:Information Networking
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• v.29 no.5
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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.