Earticle

본 연구는 기계적 학습을 이용한 주택가격 예측에 관한 연구로 선형회귀모형과 빅데이 터분석방법론인 그래디언트 부스팅과 서포트 벡터 머신의 예측력을 비교 분석하고자 한 다. 본 연구의 종속변수는 주택매매가격이고 독립변수는 주택전세가격, 소비자물가지수, 금리와 건축물착공현황으로 설정하였으며 시간적 범위는 2006년 1월부터 2020년 7월까지 로 하였다. 공간적 범위는 전국, 수도권, 지방과 서울로 하였다. 실증분석결과, 모든 지역에 서 그래디언트 부스팅모형이 평균제곱오차(MSE)값이 가장 낮은 것으로 나타나 예측력이 가장 뛰어난 것으로 나타났다. 그래디언트 부스팅 모형의 변수들의 영향력을 살펴보면, 서 울과 수도권은 주택전세가격의 영향력이 가장 높은 반면 전국과 지방에서는 소비자물가지 수의 영향력이 주택전세가격보다 높은 것으로 나타났다. 조건부 효과를 살펴보면, 지역별 로 차이는 있으나 대부분의 지역에서 주택매매가격에 대해 소비자물가지수와 주택전세가 격은 정(+)의 영향을 미치는 것으로 나타났고 건축물착공현황의 영향이 아주 미미한 것으 로 나타났다. 또한, 금리는 주택매매가격에 부(-)의 영향을 미치는 것으로 나타났다.

This study compares the predictive power of a linear regression model, a big data analysis methodology, gradient boosting, and a support vector machine. The dependent variable was the housing price, and the independent variables were the housing Jeonse price, consumer price index, interest rate and the status of the building start. The time range was from January 2006 to July 2020. The spatial scope was the whole country, the metropolitan area, provinces and Seoul. As a result of empirical analysis, the gradient boosting model showed the lowest mean square error (MSE) value in all regions, indicating that it has the best predictive power. Looking at the influence of the variables of the gradient boosting model, it was found that the influence of the Jeonse price was the highest in Seoul and the metropolitan area, while the influence of the Consumer Price Index was higher than the Jeonse price in the nation and provinces. Looking at the conditional effect, although there are regional differences, in most regions, the consumer price index and the housing jeonse price showed a positive (+) effect on the housing sale price, and the effect of the construction start status was very insignificant. Also, interest rates were found to have a negative (-) effect on the housing sale price.

In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models. It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.

무등산은 2013년 우리나라 제21호 국립공원으로 지정되었으며, 도심지역에 위치하고 있어 시민들의 여가활 동에 중요한 역할을 하고 있다. 이러한 측면에서 본 연구는 무등산을 방문한 탐방객의 만족도에 영향을 미치는 요 인은 무엇이며, 이들 중 탐방만족도에 가장 큰 영향을 미치는 요인은 무엇인지를 파악하고자 시도되었다. 그 결과 탐방만족도에 미치는 영향력은 탐방로 정비상태(β=.362)가 가장 크게 나타났으며, 그 다음으로 저렴한 여행비용 (β=.224), 다양한 특산상품(β=.184), 편리한 교통수단(β=.127) 순으로 분석되었다.

Mudeungsan was the last to be designated as a national park in 2013 and located in urban area can play important role in citizens’ leisure activities. In that respect, this study was carried out to examine the factors affecting visitors’ satisfaction and the main influence factor of visitors’ satisfaction from among these. The results showed that maintenance of trail (β=.362) affects visitors’ satisfaction most, followed by reasonable travel cost (β=.334), various local product (β=.184) and convenient transportation (β=.127).

Tourism market forecast is an important tool to analyze the tourism market trends, to understand the trends of the needs of tourism market, as well as to forecast tourism demand in the direction of regional tourism development, by which the great significance for tourism product development strategy and market strategy could be carried on. The traditional prediction methods paid much more attention to the accuracy of the forecasts, ignoring the results of the feasibility of forecasting and predicting operability, which had made it difficult to predict the results of scientific testing. This paper created a scientific evaluation system for predictive value, both to ensure the accuracy, stability of the predicted value, and to ensure the feasibility of forecasting and predicting the results of operation based on simple linear regression applied to the forecasts of the tourism market. Therefore, the outcome of the tourism market forecasts in this paper can provide a scientific basis for the government to develop the policy for tourism industry.

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

Forecasting quality of complex products is a major concern for quality engineers and enterprises' decision-makers. But only a few researchers have investigated how multiple linear regression analysis can improve forecasts. This article presents a predictive control model for personalized forecasting system of complex product quality using multiple linear regression analysis. First, this paper compares the performance of personalized forecasting system for product quality which leads to a better understanding of the applicability of the forecasting model. Second, this paper identifies the effect of predictive control model of multiple linear regression analysis on forecasting accuracy. Then, an example shows the feasibility of using predictive control model in complex product quality personalized forecasting. Finally, it makes a conclusion that the approved prediction system embraces superiority over the prediction of the quality factors and manufacturing resources.

파프리카 생산성에는 광량이 매우 중요한 요인으로 작용하나 광량을 독립변인으로 한 파프리카 생산성 추정을 위한 표준 모델 개발에 있어 어려움을 겪고 있다. 본 연구는 이러한 표준 모델을 개발할 시 독립변인으로서 외부 광 량의 활용 적합성을 높이기 위한 방법을 알아보기 위해서 수행되었다. 선형회귀 분석 시 독립변인(누적 외부광량)과 종속변인(누적 생산성)을 전체 농가 생산성의 평균값, 그리고 그 평균값을 기준으로 이상과 미만(MHFP, MLFP)으로 분류하여 각각 평균값을 활용하였다. 도출된 선형회귀모형의 RMSE 값은 MHFP의 모델에서 0.9418kg·m-2로 MTFP 모델의 1.5468kg·m-2, MLFP 모델의 1.3812kg·m-2보다 크게 낮았다. 그리고 시간(월)이 경과함에 따른 RMSE 값도 MHFP에서는 모든 월에 1.0 kg·m-2 이하로 가장 낮았다. 따라서 외부 광량을 활용한 파프리카 생산성 추정을 위한 회귀모형 개발 시 농가들의 생산성 차이를 적합한 방법으로 분류하여 분석하는 것이 추정 모델의 적합성을 향상시킬 것으로 판단된다.

The amount of sunlight (irradiation) acts as a very important factor for paprika (Capsicum annuum L.) productivity, but there are difficulties in developing a standard model for estimating paprika productivity using irradiation factors. This study was conducted to investigate how to increase the suitability of using irradiation as an independent variable when developing a standard model. In the linear regression analysis using the independent variable (cumulative irradiation) and the dependent variable (cumulative productivity) were classified as the average value of the total farm productivity (MTFP), and above and below (MHFP, MLFP) based on the average value, respectively. The RMSE value of the estimated linear regression model was 0.9418 kg·m-2 in the MHFP, which was significantly lower than 1.5468 kg·m-2 in the MTFP and 1.3812 kg·m-2 in the MLFP. And in due course of time (month), RMSE value was also the lowest in MHFP, below 1.0 kg·m-2 in all months. Therefore, when developing a regression model for estimating paprika productivity using irradiation, it is judged that it will improve the suitability of the estimation model by classifying and analyzing the difference in productivity of farms with an appropriate method.

Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R² value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R² values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.

이 연구는 북서태평양에서 여름철(7-9월) 동안 발생하는 태풍 빈도를 예측하기 위한 다중회귀모델을 4가지 원격패턴을 이용하여 개발하였다. 이 패턴은 4-5월 동안 동아시아 대륙에서의 시베리아 고기압 진동, 북태평양에서의 북태평양 진동, 호주근처의 남극진동, 적도 중앙태평양에서의 대기순환으로 대표된다. 이 통계모델은 이 모델로부터 예측된 높은 태풍발생빈도의 해와 낮은 태풍발생빈도의 해 사이에 차를 분석함으로써 검증되었다. 높은 태풍발생빈도의 해에는 다음과 같은 4가지의 아노말리 특성을 나타내었다: i) 동아시아 대륙에 고기압성 순환 아노말리(양의 시베리아 고기압진동), ii) 북태평양에 남저북고의 기압계 아노말리, iii) 호주 근처에 저기압성 순환 아노말리(양의 남극진동), iv) 봄부터 여름 동안 니뇨3.4 지역에 저기압성 순환 아노말리. 따라서 적도 서태평양에서 무역풍 아노말리는 양반구의 아열대 서태평양에 위치한 저기압성 순환 아노말리에 의해 약화되었다. 결국, 이러한 기압계 아노말리의 공간분포는 열대 서태평양에 대류를 억제하는 대신 아열대 서태평양에 대류를 강화시켰다.

This study has developed a multiple linear regression model (MLRM) for the seasonal prediction of the summer tropical cyclone genesis frequency (TCGF) over the western North Pacific (WNP) using the four teleconnection patterns. These patterns are representative of the Siberian high Oscillation (SHO) in the East Asian continent, the North Pacific Oscillation (NPO) in the North Pacific, Antarctic oscillation (AAO) near Australia, and the circulation in the equatorial central Pacific during the boreal spring (April-May). This statistical model is verified by analyzing the differences hindcasted for the high and low TCGF years. The high TCGF years are characterized by the following anomalous features: four anomalous teleconnection patterns such as anticyclonic circulation (positive SHO phase) in the East Asian continent, pressure pattern like north-high and south-low in the North Pacific, and cyclonic circulation (positive AAO phase) near Australia, and cyclonic circulation in the Nino3.4 region were strengthened during the period from boreal spring to boreal summer. Thus, anomalous trade winds in the tropical western Pacific (TWP) were weakened by anomalous cyclonic circulations that located in the subtropical western Pacific (SWP) in both hemispheres. Consequently, this spatial distribution of anomalous pressure pattern suppressed convection in the TWP, strengthened convection in the SWP instead.

In this paper, we review some class of t-norms on which fuzzy arithmetic operations preserve the shapes of fuzzy numbers and the Hausdorff-distance between fuzzy numbers as the measure of distance between fuzzy numbers. And we suggest the least Hausdorff-distance square method for fuzzy linear regression model using shape preserving fuzzy arithmetic operations.

This paper focuses on the developed empirical models for the prediction on top-bead width in GMA(Gas Metal Arc) welding process. Three empirical models have been developed: linear, curvilinear and an intelligent model. Regression analysis was employed fur optimization of the coefficients of linear and curvilinear model, while Genetic Algorithm(GA) was utilized to estimate the coefficients of intelligent model. Not only the fitting of these models were checked, but also the prediction on top-bead width was carried out. ANOVA analysis and contour plots were respectively employed to represent main and interaction effects between process parameters on top-bead width.

This paper considers a linear regression model with space and time data in where the disturbances follow spatially correlated error components. We provide the best linear unbiased predictor for the one way error components. We provide the best linear unbiased predictor for the one way error component model with spatial autocorrelation. Further, we derive two diagnostic test statistics for the assessment of model specification due to spatial dependence and random effects as an application of the Lagrange Multiplier principle.

Yen et al. [Fuzzy Sets and Systems 106 (1999) 167-177] calculated the fuzzy membership function for the output to find the non-symmetric triangular fuzzy number coefficients of a linear regression model for all given input-output data sets. In this note, we show that the result they obtained in their paper is invalid.

In this paper we consider the robust inference for the parameter of linear regression model based on weighted least squares. First we consider the sequential test of multiple outliers. Next we suggest the way to assign a weight to each observation $(x_i,\;y_i)$ and recommend the robust inference for linear model. Finally, to check the performance of confidence interval for the slope using proposed method, we conducted a Monte Carlo simulation and presented some numerical results and examples.

일종의 Autoregression Model에 강우와 유량의 입력에 의하여 일유입량의 예측을 행한 것으로 댐 지점의 일유입량과 우량시계열을 회귀분석하여 댐 유역의 하천유량을 예측 할 수 있는 수학적 모형을 수립하고 통계적 분석을 행 하고자 한다.

In the model selection problem, the main objective is to choose the true model from a manageable set of candidate models. An information criterion gauges the validity of a statistical model and judges the balance between goodness-of-fit and parsimony; "how well observed values ran approximate to the true values" and "how much information can be explained by the lower dimensional model" In this study, we introduce some information criteria modified from the Akaike Information Criterion (AIC) and the Bayesian Information Criterion(BIC). The information criteria considered in this study are compared via simulation studies and real application.

Some reasons for high leverage are analytically investigated by decomposing leverage into meaningful components. The results in this work can be used for remedial action as a next step of data analysis.

This paper consider the testing problem of grouped heteroscedasticity in the linear regression model. We provide the Lagrange Multiplier(LM), Wald, Likelihood Ratio (LR) test statistis for testing of grouped heteroscedasticity. Monte Carlo experiments are conducted to study the performance of these tests.

In this note, we propose a simple fuzzy linear regression model for fuzzy input-output data based on Tanaka's approach. Then an LP-based method to derived the satisfying solution of the decision making is developed.