• Title/Summary/Keyword: forecasting technique

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Enhancing the Performance of Call Center using Simulation (시뮬레이션을 통한 콜센터의 성능 개선)

  • 김윤배;이창헌;김재범;이계신;이병철
    • Journal of the Korea Society for Simulation
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    • v.12 no.4
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    • pp.83-94
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    • 2003
  • Managing a call center is a complex and diverse challenge. Call center becomes a very important contact point and a data ware house for successful CRM. Improving performance of call center is critical and valuable for providing better service. In this study we applied forecasting technique to estimate incoming calls and ProModel based simulation model to enhance performance of a mobile telecommunication company's call center. The simulation study shows reduction in managing cost and better customer's satisfaction.

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A probabilistic framework for drought forecasting using hidden Markov models aggregated with the RCP8.5 projection

  • Chen, Si;Kwon, Hyun-Han;Kim, Tae-Woong
    • Proceedings of the Korea Water Resources Association Conference
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    • 2016.05a
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    • pp.197-197
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    • 2016
  • Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

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Establishment of Strategy for Management of Technology Using Data Mining Technique (데이터 마이닝을 통한 기술경영 전략 수립에 관한 연구)

  • Lee, Junseok;Lee, Joonhyuck;Kim, Gabjo;Park, Sangsung;Jang, Dongsik
    • Journal of the Korean Institute of Intelligent Systems
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    • v.25 no.2
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    • pp.126-132
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    • 2015
  • Technology forecasting is about understanding a status of a specific technology in the future, based on the current data of the technology. It is useful when planning technology management strategies. These days, it is common for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts' opinions. However, this qualitative method of technology forecasting is costly and time consuming since it requires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of technology forecasting based on quantitative analysis. The methodology consists of data collection, principal component analysis, and technology forecasting by logistic regression, which is one of the data mining techniques. In this research, patent documents related to autonomous vehicle are collected. Then, the texts from patent documents are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performed by using principal component score. On the basis of this result, it is possible to analyze R&D development situation and technology forecasting.

Predictive Modeling of River Water Quality Factors Using Artificial Neural Network Technique - Focusing on BOD and DO- (인공신경망기법을 이용한 하천수질인자의 예측모델링 - BOD와 DO를 중심으로-)

  • 조현경
    • Journal of Environmental Science International
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    • v.9 no.6
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    • pp.455-462
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    • 2000
  • This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

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A novel Kohonen neural network and wavelet transform based approach to Industrial load forecasting for peak demand control (최대수요관리를 위한 코호넨 신경회로망과 웨이브릿 변환을 이용한 산업체 부하예측)

  • Kim, Chang-Il;Yu, In-Keun
    • Proceedings of the KIEE Conference
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    • 2000.07a
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    • pp.301-303
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    • 2000
  • This paper presents Kohonen neural network and wavelet transform analysis based technique for industrial peak load forecasting for the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a six-scale synthesis technique.

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Development of Typhoon Damage Forecasting Function of Southern Inland Area By Multivariate Analysis Technique (다변량 통계분석을 이용한 남부 내륙지역 태풍피해예측모형 개발)

  • Kim, Yonsoo;Kim, Taegyun
    • Journal of Wetlands Research
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    • v.21 no.4
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    • pp.281-289
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    • 2019
  • In this study, the typhoon damage forecasting model was developed for southern inland district. The typhoon damage in the inland district is caused by heavy rain and strong winds, variables are many and varied, but the damage data of the inland district are not enough to develop the model. The hydrological data related to the typhoon damage were hour maximum rainfall amount which is accumulated 3 hour interval, the total rainfall amount, the 1-5 day anticipated rainfall amount, the maximum wind speed and the typhoon center pressure at latitude 33° near the Jeju island. The Multivariate Analysis such as cluster Analysis considering the lack of damage data and principal component analysis removing multi-collinearity of rainfall data are adopted for the damage forecasting model. As a result of applying the developed model, typhoon damage estimated and observed values were up to 2.2 times. this is caused it is difficult to estimate the damage caused by strong winds and it is assumed that the local rainfall characteristics are not considered properly measured by 69 ASOS.

Weather Prediction Using Artificial Neural Network

  • Ahmad, Abdul-Manan;Chuan, Chia-Su;Fatimah Mohamad
    • Proceedings of the IEEK Conference
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    • 2002.07a
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    • pp.262-264
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    • 2002
  • The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

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A study on the Electrical Load Pattern Classification and Forecasting using Neural Network (신경회로망을 이용한 전력부하의 유형분류 및 예측에 관한 연구)

  • Park, June-Ho;Shin, Gil-Jae;Lee, Hwa-Suk
    • Proceedings of the KIEE Conference
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    • 1991.11a
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    • pp.39-42
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    • 1991
  • The Application of Artificial Neural Network(ANN) to forecast a load in a power system is investigated. The load forecasting is important in the electric utility industry. This technique, methodology based on the fact that parallel structure can process very fast much information is a promising approach to a load forecasting. ANN that is highly interconnected processing element in a hierachy activated by the each input. The load pattern can be divided distinctively into two patterns, that is, weekday and weekend. ANN is composed of a input layer, several hidden layers, and a output layer and the past data is used to activate input layer. The output of ANN is the load forecast for a given day. The result of this simulation can be used as a reference to a electric utility operation.

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Real-time Flood Forecasting Model for Irrigation Reservoir Using Simplex Method (최적화기법에 의한 관개저수지의 실시간 홍수예측모형)

  • 문종필;김태철
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.43 no.2
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    • pp.85-93
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    • 2001
  • The basic concept of the model is to minimize the error range between forecasted flood inflow and actual flood inflow, and forecast accurately the flood discharge some hours in advance depending on the concentration time(Tc) and soil moisture retention storage(Sa). Simplex method that is a multi-level optimization technique was used to search for the determination of the best parameters of RETFLO (REal-Time FLOod forecasting) model. The flood forecasting model developed was applied to several strom event of Yedang reservoir during past 10 years. Model perfomance was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

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한국과 미국간 항공기 탑승객 수 예측을 위한 뉴럴네트웍의 응용

  • 남경두
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1995.09a
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    • pp.334-343
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    • 1995
  • In recent years, neural networks have been developed as an alternative to traditional statistical techniques. In this study, a neural network model was compared to traditional forecasting models in terms of their capabilities to forecast passenger traffic for flights between U.S. and Korea. The results show that the forecasting ability of the neural networks was superior to the traditional models. In terms of accuracy, the performance of the neural networks was quite encouraging. Using mean absolute deviation, the neural network performed best. The new technique is easy to learn and apply with commercial neural network software. Therefore, airline decision makers should benefit from using neural networks in forecasting passenger loads.

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