• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.1-10
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• 2013

This study was conducted to investigate that bus information was used as an information of travel speed. To determine the travel speed on the road, bus information and the information collected from the point detector and the interval detection installed were compared. If bus information has the function of traffic information detector, can provide the travel speed information to road users. To this end, the model of recognizing the traffic patterns is necessary. This study used simple moving-average method, simple exponential smoothing method, Double moving average method, Double exponential smoothing method, ARIMA(Autoregressive integrated moving average model) as the existing methods rather than new approach methods. This study suggested the possibility to replace bus information system into other information collection system.

• International Journal of Quality Innovation
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• v.2 no.1
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• pp.69-86
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• 2001

The economic design of control charts has been researched for over four decades since Duncan proposed the concept in 1956. Few studies, however, have focused attention on the economic design of a moving average (MA) control chart. An MA control chart is more effective than the Shewhart chart in detecting small process shifts [9]. This paper provides an economic model for determining the optimal parameters of an MA control chart with multiple assignable causes and two failures in the production process. These parameters consist of the sample size, the spread of the control limit and the sampling interval. A numerical example is shown and the sensitivity analysis shows that the magnitude of shift, rate of occurrence of assignable causes and increasing cost when the process is out of control have a more significant effect on the loss cost, meaning that one should more carefully estimate these values when conducting an economic analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.7 no.1
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• pp.21-31
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• 1981

This paper develops a short term forecasting model for household electric power consumption in Seoul, which can be used for the effective planning and control of utility management. The model developed is based on exponentially weighted moving average model and incorporates monthly average temperature as an exogeneous factor so as to enhance its forecasting accuracy. The model is empirically compared with the Winters' three parameter model which is widely used in practice and the Box-Jenkins model known to be one of the most accurate short term forecasting techniques. The result indicates that the developed hybrid exponential model is better in terms of accuracy measured by average forecast error, mean squared error, and autocorrelated error.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.211-218
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• 1998

In start-up process control applications it may be necessary to limit the sample size to one measurement. A control chart for individual measurements is used whenever it is desirable to examine each individual value from the process immediately. A possible option would be to use an exponential weighted moving(EWM), using modifying statistics with individual measurement, chart for monitoring the process center, and using a moving range (MR) chart for monitoring process variability. In this paper it is shown that there is scheme in using the EWM procedure based on average run length. An expression for the ARL is given in terms of an integral equation, approximated using numerical quadrature. In this case, where it is reasonable to assume normality and negligible autocorrelation in the observations, provide graphs that simplify the design of EWM-MR chart and taking method of exponential smoothing constant(λ) and constant(K) are suggested. The charts suggested above evaluate using the conditional probability.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.

• Journal of KIISE
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• v.42 no.7
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• pp.939-945
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• 2015

From 2009, the market of smart devices has been rapidly increasing. These devices provide various services to users. The cloud messaging service, especially, is applied to many various services, and sends messages asynchronously. In the cloud messaging service, there are two methods for message transmission, message transmission based on an IP address and a publish/subscribe technique. Each technique uses basic messages in order to send messages to mobile devices. In this paper, the hybrid message transmission, based on user-customized analysis to reduce basic messages, is proposed. The hybrid message transmission uses Exponential Moving Average (EMA) and K-means algorithms for user-customized analysis, and determines the message transmission techniques in each timeslot.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.645-655
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• 2022

In this paper, we investigate automatic functions for time series forecasting in R system and compare their performances. For the exponential smoothing models and ARIMA (autoregressive integrated moving average) models, we focus on the representative time series forecasting functions in R: forecast::ets(), forecast::auto.arima(), smooth::es() and smooth::auto.ssarima(). In order to compare their forecast performances, we use M3-Competiti on data consisting of 3,003 time series and adopt 3 accuracy measures. It is confirmed that each of the four automatic forecasting functions has strengths and weaknesses in the flexibility and convenience for time series modeling, forecasting accuracy, and execution time.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.141-155
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• 2005

It is very important that sellers provide reasonable reserve prices for auction items in internet auction systems. Recently, an agent has been proposed to generate reserve prices automatically based on the case similarity of information retrieval theory and the moving average of time series analysis. However, one problem of the previous approaches is that the recent trend of auction prices is not well reflected on the generated reserve prices, because it simply provides the bid price of the most similar item or an average price of some similar items using the past auction data. In this paper. in order to overcome the problem. we propose a method that generates reserve prices based on the moving average. the exponential smoothing, and the least square of time series analysis. Through performance experiments. we show that the successful bid rate of the new method can be increased by preventing sellers from making unreasonable reserve prices compared with the previous methods.

• 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.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.287-290
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• 2000

This paper analyze link-sharing mechanisms in packet networks based on the hierarchical class based queueing. The CBQ outlines a set of flexible, efficiently implemented gateway mechanisms that can meet a range of service and link-sharing requirements. We have analyzed the Class level(B, C, D) using the EWMA (Exponential Weighted Moving Average) weight value and EWMA average limit value.