• Journal of Navigation and Port Research
• /
• v.42 no.5
• /
• pp.341-346
• /
• 2018

Supramax bulk carriers cover a wide range of ocean transportation requirements, from major to minor bulk cargoes. Market forecasting for this segment has posed a challenge to researchers, due to complexity involved, on the demand side of the forecasting model. This paper addresses this issue by using technical indicators as input features, instead of complicated supply-demand variables. Artificial neural networks (ANN), one of the most popular machine-learning tools, were used to replace classical time-series models. Results revealed that ANN outperformed the benchmark binomial logistic regression model, and predicted direction of the spot market with more than 70% accuracy. Results obtained in this paper, can enable chartering desks to make better short-term chartering decisions.

• Journal of Digital Convergence
• /
• v.19 no.5
• /
• pp.241-247
• /
• 2021

This study analyzed the changes in explanatory power of the modified Jones model(1995) for estimating the amount of accruals for Korean Stock Market listed companies from 1990 to 2019. We hypothesized that if the properties of financial variables used in the existing model change over time or change in discretionary ratios, the model's explanatory power will change. As the result of regression models, I found that the explanatory power of the modified Jones model(1995) gradually declined over time. The results may be derived from the increase in accruals itself and the changes in the distribution of variables contained in the model. The results of this research's chronological approach are expected to give important implications to both academic researchers and accounting information users.

• Journal of the Korean Society for Library and Information Science
• /
• v.58 no.2
• /
• pp.251-268
• /
• 2024

Recently, with the implementation of the "e-Government Performance Management Guidelines," there is a growing demand for setting performance indicators for information systems. For systems that provide information services to the public, such as CODIL, it is not easy to set performance indicators. This study presented a research model that applies Monte Carlo simulation to set expected performance targets that can be achieved through CODIL based on objective evidence. Among the survey contents conducted from 2015 to 2023, the statistical characteristics of user satisfaction regarding the frequency of use of construction technology information provided by CODIL were designated as input variables. Future expected targets and confidence intervals from 2024 to 2026 were designated as outcome variables. The expected target value was measured by generating 5 simulation alternatives and 1,000 random numbers for each alternative. Next, the measured expected goals were interpreted and compared with the results of time series regression analysis measured in previous studies. Although, as in previous studies, the expected target value could not be predicted based on time series regression analysis that considers the correlation between years. However, compared to previous studies, this study can be considered a more accurate analysis result because it predicted the expected target value based on 5,000 input variables.

• Korean Journal of Health Education and Promotion
• /
• v.31 no.5
• /
• pp.45-58
• /
• 2014

Objective: The purpose of this study was to analysis the determinants and trend of suicidal ideation the elderly in Korea. Methods: This study participants were selected the elderly over the age of 55 from the Korea National Health and Nutrition Examination Survey in 2001(n=1,122), 2005(n=2,098), and 2010(n=2,402). Statistical analysis methods used in this study were $x^2$-test, logistic regression analysis and other basic statistics such frequency, percentage using SPSS version 21.0. Results: In 2001, the influencing factors of suicidal ideation was spouses, subjective health status and stress recognition. In 2005, the influencing factors of suicidal ideation were spouses, subjective health status, chronic disease amount, activity limitation, depression experience and stress recognition. In 2010, the influencing factors of suicidal ideation were elderly, education level, subjective health status, activity limitation, depression experience and stress recognition. Conclusions: The health education considering the characteristics of each elderly group should be developed and applied to prevent adults' suicidal ideation because the factors influencing suicidal ideation were revealed differently between the elderly group.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.727-734
• /
• 2021

The study aims to examine and analyze the factors that affect the return on assets (ROA) by placing net interest margin (NIM) as a moderating variable in influencing ROA. This research was conducted on 27 banks listed on the Indonesia Stock Exchange (IDX) for the period 2015 to 2018 with a total sample data of 91. The data used is a combination of time series data and cross-section data. The sampling technique used was the purposive sampling method. The data analysis technique used was path analysis with multiple regression analysis technique. The results of the analysis showed that the capital adequacy ratio (CAR) and loan to deposit ratio (LDR) have a positive but insignificant effect on ROA. NIM as a moderating variable does not influence the impact of CAR on ROA. However, NIM as a moderating variable is able to influence the impact of LDR on ROA. From the results of this study, it is evident that the LDR will increase the ROA at banks that generate high NIM.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.49 no.6
• /
• pp.77-84
• /
• 2007

This study aims to analyze temporal variation of rural tourist potential in village level. In order to analyze the temporal variation, this study applied a green-tourism potential evaluation model(GPEM) to a study area, Namilmyun with 18 villages, which located on county of Kumsan, province of Chungnam. GPEM consists of two factors about human resources, which is quantified by resident population of the village who will be participated in village management for green-tourism, and amenity resources, which is calculated by an evaluation table with 31 criteria and their weighting values. Data surveying for the study area was performed at August 2003 and 2006, respectively, in order to quantity the 31 evaluation criteria of GPEM. From the analysis results, the amenity resources with three sub-classes of industrial, natural, and social resources showed that the evaluation values of 2006 were increased in more than those of 2003, displaying the increase rate of ranging from 108% to 112% in the three sub-classes, except of one village in social resources and three village in natural resources with reduction. In human resources, the evaluation values of 2006 were highly increased more than those of 2003, showing the rate of 556% in the gradient of linear regression line. In green tourism potential in each village, the evaluation results for two time series showed that the potential is increased by time, which the increase rate of the potential is 114%. Ultimately, the results of this study enable us to realize that the green-tourism potential in each village over time is increased, for example, due to developing new facilities in the village by investment of government and participating in rural tourism by resident people over time. From the analysis and results, the methodology of this study can be applied to analyze the temporal variation of the potential for villages having investment from government.

• Journal of the Economic Geographical Society of Korea
• /
• v.16 no.4
• /
• pp.631-646
• /
• 2013

This paper empirically explores the pattern of causality between bank lending and housing prices in Korea over a period of the early 1990s to the end of 2000s by employing a long term cointegration and short-term time series regression analysis. Although the contemporaneous correlation between bank lending and housing prices is large, the analysis shows that the intense interaction between credit growth and bank lending to household arises from a growth in banking lending responding to an increase in housing prices. In addition, the regulatory change such as the introduction of financial constraints on bank loans such as LTV and DTI in the early and mid-2000s has played a significant role in stabilizing financial and real estate markets.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.1
• /
• pp.87-94
• /
• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.1
• /
• pp.117-133
• /
• 2014

In this study, we analysed impacts of the recent increasing trend of exceedance rainfall thresholds for separation of data set and different research periods using Quantile Regression (QR) approach. And also we performed significant test for time series data using linear regression, Mann-Kendall test and Sen test over the Korean major 8-city. Spring and summer precipitation was tend to significant increase, fall and winter precipitation was tend to decrease, and heavy rainy days in last 30 years have increased from 3.1 to 15 percent average. In addition, according to the annual ranking of rainfall occurs Top $10^{th}$ percentile of precipitation for 3IQR (inter quartile range) of the increasing trend, most of the precipitation at the point of increasing trend was confirmed. Quantile 90% percentile of the average rainfall 43.5mm, the increasing trend 0.1412mm/yr, Quantile 99% percentile of the average rainfall 68.0mm, the increasing trend in the 0.1314mm/yr were analyzed. The results can be used to analyze the recent increasing trend for the annual maximum value series information and the threshold extreme hydrologic information. And also can be used as a basis data for hydraulic structures design on reflect recent changes in climate characteristics.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.2
• /
• pp.383-388
• /
• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.