• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.257-263
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• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• Journal of Distribution Science
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• v.14 no.11
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• pp.83-90
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• 2016

Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.177-183
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• 2011

Korea has performed strategies for the third industrial accident prevention in order to minimize industrial accident. However, the occupational fatality rate and industrial accident rate appears to be stagnated for 11 years. Therefore, this paper forecasts the occupational fatality rate and industrial accident rate for 10 years. Also, this paper applies regression method (RA), exponential smoothing method (ESM), double exponential smoothing method (DESM), autoregressive integrated moving average (ARIMA) model and proposed analytical function method (PAFM) for trend of industrial accident. Finally, this paper suggests fundamental strategies for industrial accident prevention by forecasting of industrial accident rate in the long term.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.378-385
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• 2005

Mistaking normal packets for harmful traffic may not offer service in conformity with the intention of attacker with harmful traffic, because it is not easy to classify network traffic for normal service and it for DDoS(Distributed Denial of Service) attack. And in the IPv6 environment these researches on harmful traffic are weak. In this dissertation, hosts in the IPv6 environment are attacked by NETWOX and their attack traffic is monitored, then the statistical information of the traffic is obtained from MIB(Management Information Base) objects used in the IPv6. By adapting the ESM(Exponential Smoothing Method) to this information, a normal traffic boundary, i.e., a threshold is determined. Input traffic over the threshold is thought of as attack traffic.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.87-94
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• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.185-192
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• 2006

By mistaking normal packets for harmful traffic, it may not offer service according to the intention of attacker with harmful traffic, because it is not easy to classify network traffic for normal service and it for DUoS(Distributed DoS) attack like the Internet worm. And in the IPv6 environment these researches on harmful traffic are weak. In this dissertation, hosts in the IPv6 environment are attacked by NETWIB and their attack traffic is monitored, then the statistical information of the traffic is obtained from MIB(Management Information Base) objects used in the IPv6. By adapting the ESM(Exponential Smoothing Method) to this information, a normal traffic boundary, i.e., a threshold is determined. Input traffic over the threshold is thought of as attack traffic.

• Korean Security Journal
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• no.30
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• pp.139-169
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• 2012

The purpose of this study is to contribute to establishing the scientific policing policies through deriving the time series models that can forecast the occurrence of major crimes such as murder, robbery, burglary, rape, violence and identifying the occurrence of major crimes using the models. In order to achieve this purpose, there were performed the statistical methods such as Generation of Time Series Model(C) for identifying the forecasting models of time series, Generation of Time Series Model(C) and Sequential Chart of Time Series(N) for identifying the accuracy of the forecasting models of time series on the monthly incidence of major crimes from 2002 to 2010 using IBM PASW(SPSS) 19.0. The following is the result of the study. First, murder, robbery, rape, theft and violence crime's forecasting models of time series are Simple Season, Winters Multiplicative, ARIMA(0,1,1)(0,1,1), ARIMA(1,1,0 )(0,1,1) and Simple Season. Second, it is possible to forecast the short-term's occurrence of major crimes such as murder, robbery, burglary, rape, violence using the forecasting models of time series. Based on the result of this study, we have to suggest various forecasting models of time series continuously, and have to concern the long-term forecasting models of time series which is based on the quarterly, yearly incidence of major crimes.