• Communications for Statistical Applications and Methods
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• 제21권6호
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• pp.471-486
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• 2014

This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries'. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.

• 동아시아경상학회지
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• 제10권2호
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• pp.31-42
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• 2022

Purpose - The main research goals by macroeconomic analysis is to assess the effectiveness of state regulation, the sustainability of development, and the financial stability of the state. Research design, Data, and methodology - The research were analyzed using the methods of multivariate statistics and application of the software package Stat graphics. The volume of data from the 1995 to the 2021 was analyzed by Russian Federation. The scale of research on Belarus: to be analyzed the amount of data from the 2015 by 2021, on Kazakhstan - from the 19941, on Kyrgyzstan - from the 2002, on Tajikistan - from the 2008, on Armenia - from the 2021, on Japan - since the 1970, on China - since the 1950, on South Korea - since the 1953. Result - The methods of multivariate statistics was demonstrated exact of result in forecasting of macroeconomic indicators. The most of tendency with the accurate results of are described using the second-degree polynomials. In the most research of country there are the macroeconomic proportion are broken. Conclusion - In the countries studied, the monetary aggregates have a significant growth rate. The shares with a substantial monetary stock and the speed of its growth are divided in the two groups: having placements in the real sectors of the economy and not having received the same result of development from the growth of the monetary stock.

• ETRI Journal
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• 제37권2호
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• pp.283-294
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• 2015

A model that precisely forecasts how much wind power is generated is critical for making decisions on power generation and infrastructure updates. Existing studies have estimated wind power from wind speed using forecasting models such as ANFIS, SMO, k-NN, and ANN. This study applies a projected clustering technique to identify wind power patterns of wind turbines; profiles the resulting characteristics; and defines hourly and daily power patterns using wind power data collected over a year-long period. A wind power pattern prediction stage uses a time interval feature that is essential for producing representative patterns through a projected clustering technique along with the existing temperature and wind direction from the classifier input. During this stage, this feature is applied to the wind speed, which is the most significant input of a forecasting model. As the test results show, nine hourly power patterns and seven daily power patterns are produced with respect to the Korean wind turbines used in this study. As a result of forecasting the hourly and daily power patterns using the temperature, wind direction, and time interval features for the wind speed, the ANFIS and SMO models show an excellent performance.

• 산업공학
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• 제24권4호
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• 응용통계연구
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• 제28권6호
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• pp.1235-1243
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• 2015

최근 들어 ICT 분야의 발달에 따라 데이터 사용량의 급격한 증가로 인터넷 트래픽 사용량 예측은 중요성은 강조되고 있다. 이러한 예측치를 적절한 트래픽 관리와 제어를 위한 계획 수립에 도움을 준다. 본 논문은, 5분 단위의 인터넷 트래픽 자료를 이용하여 결합 예측 모형을 제안하고자 한다. 이에 대하여 시계열의 대표적인 3개 모형인 Seasonal ARIMA, Fractional ARIMA(FARIMA), Taylor의 수정된 Holt-Winters 모형을 적용하였다. 모형 간 결합 예측 방법으로 예측치 간의 SA(Simple Average) 결합 예측 방법과 OLS(Ordinary Least Square)를 이용한 결합방법, ERLS(Equality Restricted Least Squares)를 이용한 결합 예측 방법, Armstrong(2001)이 제안한 MSE 기반 결합 예측 방법을 사용한다. 이에 따른 결과로서 3시간에서의 예측은 Seasonal ARIMA가 선택된 반면, 6시간 이후 예측에서는 결합 예측 방법이 좋은 예측 성능을 보여준다.

• 조명전기설비학회논문지
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• 제21권7호
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• pp.61-67
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• 2007

전력수요예측은 전력계통의 운용을 위해 필수적이다. 따라서 다양한 방법이 제시되어 왔으며, 특히 특수일의 수요예측은 평일과 구분되며, 부하 패턴을 축출하기에 충분한 자료 확보가 어려워 예측 오차가 크게 나타난다. 본 논문에서는 특수일의 부하예측 정확도를 개선하기 위해 퍼지 최소자승 선형회귀 모델을 분석한다. 4종류의 퍼지 최소자승 선형회귀 모델에 대해 분석과 사례연구를 통하여 가장 정확한 모델을 제시한다.

• Journal of the Korean Data and Information Science Society
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• 제24권2호
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• pp.311-320
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• 2013

본 연구의 목적은 학생 (인구)수를 예측하기 위한 방법을 제시하는데 있다. 일반화된 가중비례모형들을 제시하고, 제시된 모형들을 이용하여 2029년까지의 학생 (인구)수를 추계하였다. 몬테칼로 모의실험의 결과 제시된 가중비례추정모형은 인구예측에 있어서 상당한 신뢰성과 예측력을 보인다. 결론적으로 추정된 고등학교 3학년 학생수는 대학들의 입학정원 수와 비교하면, 2019년부터 고3 학생수가 적게 나타나지만, 현재 대학들의 실제 입학자 수와 비교하면 2014년부터 입학생 수가 부족한 현상이 나타난다. 그러므로 입학정원과 입학정원 외의 신입생 수를 줄이지 않는 경우에는 2015년부터 입학생을 채우지 못하는 학교들이 생겨나게 될 것이다.

• 한국환경과학회지
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• 제22권8호
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• 융합보안논문지
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• 제8권2호
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• pp.35-40
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• 2008

소트프웨어 고장 시간은 테스팅 시간과 관계없이 일정하거나, 단조 증가 혹은 단조 감소 추세를 가지고 있다. 이러한 소프트웨어 신뢰모형들을 분석하기 위한 자료척도로 자료에 대한 추세 검정이 개발되어 있다. 추세 분석에는 산술평균 검정과 라플라스 추세 검정 등이 있다. 추세분석들은 전체적인 자료의 개요의 정보만 제공한다. 본 논문에서는 고장시간을 측정하다가 시간절단이 될 경우에 미래의 고장 시간 예측에 관하여 연구되었다. 고장 시간 예측에 사용된 고장시간자료는 소프트웨어 고장 시간 분포에 널리 사용되는 와이블 분포에서 형상모수가 1이고 척도모수가 0.5를 가진 난수를 발생된 모의 자료를 이용 하였다. 이 자료를 이용하여 시계열 분석에 이용되는 ARIMA 모형 중에서 AR(1) 모형과 모의실험을 통한 예측 방법을 제안하였다. 이 방법에서 ARIMA 모형을 이용한 예측방법이 효율적임을 입증 하였다.