• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.197-215
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• 2021

This paper analyzes the machine learning predictions of the economic effects of Busan's strategic industries on the employment and income using the Ridge Regression and Lasso Regression models with regulation terms. According to the Ridge estimation and Lasso estimation models of employment, the intelligence information service industry such as the service platform, contents, and smart finance industries and the global tourism industry such as MICE and specialized tourism are predicted to influence on the employment in order. However, the Ridge and Lasso regression model show that the future transportation machine industry does not significantly increase the employment and income since it is the primitive investment industry. The Ridge estimation models of the income show that the intelligence information service industry and global tourism industry are also predicted to influence on the income in order. According to the Lasso estimation models of income, four strategic industries such as the life care, smart maritime, the intelligence machine, and clean tech industry do not influence the income. Furthermore, the future transportation machine industry may influence the income negatively since it is the primitive investment industry. Thus, we have to select the appropriate economic objectives and priorities of industrial policies.

• Smart Media Journal
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• v.11 no.5
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• pp.9-16
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• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.