• Title/Summary/Keyword: 지수평활법

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Time series regression model for forecasting the number of elementary school teachers (초등학교 교원 수 예측을 위한 시계열 회귀모형)

  • Ryu, Soo Rack;Kim, Jong Tae
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.2
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    • pp.321-332
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    • 2013
  • Because of the continuous low birthrates, the number of the elementary students will decrease by 17% in 2020 compared to 2011. The purpose of this study is to forecast the number of elementary school teachers until 2020. We used the data in education statistical year books from 1970 to 2010. We used the time-series regression model, time series grouped regression model and exponential smoothing model to predict the number of teachers for the next ten years. Consequently time-series grouped regression model is a better model for forecasting the number of elementary school teachers than other models.

Forecasted Popularity Based Lazy Caching Strategy (예측된 선호도 기반 게으른 캐싱 전략)

  • Park, Chul;Yoo, Hae-Young
    • 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.

Comparison of time series predictions for maximum electric power demand (최대 전력수요 예측을 위한 시계열모형 비교)

  • Kwon, Sukhui;Kim, Jaehoon;Sohn, SeokMan;Lee, SungDuck
    • The Korean Journal of Applied Statistics
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    • v.34 no.4
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    • pp.623-632
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    • 2021
  • Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

Fuzzy Demand Control System (퍼지제어형 최대수요전력 관리시스템)

  • 안준식;조성원;윤상철;신성철;성기철;한홍석
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.241-246
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    • 1997
  • 본 논문에서는 사용이 편리하여 산업 발전의 원동력이 되고 있는 전기에너지의 이용합리화의 수단의 하나인 디맨드 감시/제어 장치에 대한 퍼지제어의 적용에 대해 제안한다. 이 장치는 전력사용이 많은 300㎾이상의 수용가에 있어서 피크(Peak)시 전력이 계약전력을 초과하지 않도록 감시/제어하여 에너지 사용의 합리화 및 전기요금 절감효과를 도모할수 있는 에너지 기기의 개발에 기여할 수 있다. 본 연구에서는 효율적인 디맨드 감시/제어를 위해 지수평활법을 이용한 부하예측과 퍼지제어기를 제안하고자 한다.

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Mining the Up-to-Moment Preference Model based on Partitioned Datasets for Real Time Recommendation (실시간 추천을 위한 분할셋 기반 Up-to-Moment 선호모델 탐색)

  • Han, Jeong-Hye;Byon, Lu-Na
    • Journal of Internet Computing and Services
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    • v.8 no.2
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    • pp.105-115
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    • 2007
  • The up-to-moment dataset is built by combining the past dataset and the recent dataset. The proposal is to compute association rules in real time. This study proposed the model, $EM_{past'}$ and algorithm that is sensitive to time. It can be utilized in real time by applying partitioned combination law after dividing the past dataset into(k-1). Also, we suggested $EM^{ES}_{past}$ applying the exponential smoothing method to $EM^p_{past'}$ When the association rules of $EM_{past'}\;EM^w_{past'\;and\;EM^{ES}_{past}$ were compared, The simulation results showed that $EM^{ES}_{past}$ is most accurate for testing dataset than $EM_{past}$ and $EM^w_{past}$ in huge dataset.

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The proposed algorithm for the student numbers in local government (기초자치단체의 학생수 추계를 위한 알고리즘)

  • Kim, Jong-Tae
    • 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.

Low-Latency Handover Scheme Using Exponential Smoothing Method in WiBro Networks (와이브로 망에서 지수평활법을 이용한 핸드오버 지연 단축 기법)

  • Pyo, Se-Hwan;Choi, Yong-Hoon
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.8 no.3
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    • pp.91-99
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    • 2009
  • Development of high-speed Internet services and the increased supply of mobile devices have become the key factor for the acceleration of ubiquitous technology. WiBro system, formed with lP backbone network, is a MBWA technology which provides high-speed multimedia service in a possibly broader coverage than Wireless LAN can offer. Wireless telecommunication environment needs not only mobility support in Layer 2 but also mobility management protocol in Layer 3 and has to minimize handover latency to provide seamless mobile services. In this paper, we propose a fast cross-layer handover scheme based on signal strength prediction in WiBro environment. The signal strength is measured at regular intervals and future value of the strength is predicted by Exponential Smoothing Method. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency is reduced. Simulation results demonstrate that the proposed scheme predicts that future signal level accurately and reduces the total handover latency.

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Application of Time-Series Model to Forecast Track Irregularity Progress (궤도틀림 진전 예측을 위한 시계열 모델 적용)

  • Jeong, Min Chul;Kim, Gun Woo;Kim, Jung Hoon;Kang, Yun Suk;Kong, Jung Sik
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.25 no.4
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    • pp.331-338
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    • 2012
  • Irregularity data inspected by EM-120, an railway inspection system in Korea includes unavoidable incomplete and erratic information, so it is encountered lots of problem to analyse those data without appropriate pre-data-refining processes. In this research, for the efficient management and maintenance of railway system, characteristics and problems of the detected track irregularity data have been analyzed and efficient processing techniques were developed to solve the problems. The correlation between track irregularity and seasonal changes was conducted based on ARIMA model analysis. Finally, time series analysis was carried out by various forecasting model, such as regression, exponential smoothing and ARIMA model, to determine the appropriate optimal models for forecasting track irregularity progress.

Predictive Modeling of the Bus Arrival Time on the Arterial using Real-Time BIS Data (실시간 BIS자료를 이용한 간선도로의 버스도착시간 예측모형구축에 관한 연구)

  • Kim, Tae Gon;Ahn, Hyeun Chul;Kim, Seung Gil
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.1D
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    • pp.1-9
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    • 2009
  • Bus information system(BIS), as a part of the intelligent transportation system(ITS), is one of the most advanced public transportation systems which provide the real-time bus traffic information for the users waiting the buses at the bus stop. However, correct bus information data, such as the present bus location, the user waiting time, the bus arrival time, etc. are not provided for the bus users because the proper bus arrival time predictive models are not used yet in most of the cities operating the bus information system, including the metropolitan City of Ulsan. Thus, the purpose in this study is to investigate real-time bus traffic characteristic data for identifying the bus operation characteristics on the arterial under the study in the metropolitan City of Ulsan, analyze real-time bus traffic characteristic data on the ID locations of the arterial under the study, construct the optimal unit segment models for the unit segments which are the bus stop, node and travel section using the exponential smoothing, weighted smoothing and Kalman Filter methods, respectively, and finally suggest the optimal integrated model for predicting the real-time bus arrival time at the bus stop of the arterial under the study.

A Study on the Travel Speed Estimation Using Bus Information (버스정보기반 통행속도 추정에 관한 연구)

  • Bin, Mi-Young;Moon, Ju-Back;Lim, Seung-Kook
    • 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.