• The Korean Journal of Applied Statistics
• /
• v.31 no.4
• /
• pp.427-446
• /
• 2018

Korea will have a super-aged society within only 30 years according to the United Nations' definition of an aging society and the statistics on Korea's Population projections (2016), indicates that Korea has the fastest ageing speed in the world. There is a lack of data on long-term time-series data on death as related to pension and welfare policies compared to the rapid rate of aging. This paper estimates life expectancy over 245 years (from 1955 to 2200) through past and future forecasts as well as compares the expected life expectancy of the synthetic cohort and the real cohort. In addition, an international comparisons were made to understand the level of aging in Korea. Estimates of the back-projection period were compared with previous studies and the LC model to improve accuracy and objectivity. In addition, the predictions after 2016 reflected the declined mortality rate effect of Korea using the LC-ER model. The results showed an increase in life expectancy of about 30 years over 60 years (1955-2015) with an expected life expectancy of the real cohort over the second century (1955-2155) higher than the synthetic cohort. The comparative advantage of life expectancy of real cohorts was confirmed to be a common trend among comparative countries. In addition, Japan and Korea have a higher life expectancy and starting from 85 to 90 years old, all comparative countries show that the growth rate for the life expectancy of synthetic and real cohorts is less than previous years.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.1
• /
• pp.10-21
• /
• 2021

This study analyzed how external variables, such as the supply policy of shared bicycles and the spread of infectious diseases, affect the demand for shared bicycle use in the COVID-19 era. In addition, this paper presents a methodology for more accurate predictions. The Seasonal Auto-Regulatory Integrated Moving Average with Exogenous stressors methodology was applied to capture the effects of exogenous variables on existing time series models. The exogenous variables that affected the future demand for shared bicycles, such as COVID-19 and the supply of public bicycles, were statistically significant. As a result, from the supply volume and COVID-19 outbreak according to the scenario, it was estimated that approximately 46,000 shared bicycles would be supplied by 2022, and the COVID-19 cases would continue to be at the current level. In addition, approximately 32 million and 45 million units per year will be needed in 2021 and 2024, respectively.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.4
• /
• pp.149-158
• /
• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• Journal of Korean Society of Forest Science
• /
• v.77 no.3
• /
• pp.322-330
• /
• 1988

This study was analyzed the behavior of recreational use through interviewing visitors with the questionnaire (1986.11-1987.9) in Mt. Kyeryong National Park. The number of visitors have been forecasted by tune series data of the past number of visitors, population, GNP, and number of cars (1974-1986) in korea. The results of the study can be summarized as follows : 1) Visitor's subjective evaluation about recreational environment evaluated to be fair in Mt. Kyeryong National Park. 2) They preferred natural forest resources to historic remains, tourist facility, etc.. 3) Number of participation was mostly once or five times over. 4) Visitors were affirmative to re-visit to the Mt. Kyeryong National Park. 5) Most of visitors stay for one day. 6) The most suitable estimated user regression model was : Y=-5753.7350+0.1726 Pop. -0.6564 NO. of Car. According to this equation, the total number of visitors will he increased by 3% per year from 1,023 thousands people in 1987 to 1,698 thousands in 2000.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.2
• /
• pp.385-391
• /
• 2000

Recently, neural network research for forecasting the consecutive controlling rules of the future is being progressed, using the series data which are different from the traditional statistical analysis methods. In this paper, we suggest the pruning algorithm for the fast and exact weather forecast that excludes the hidden layer of the early optional designed nenral network. There are perform the weather forecast experiments using the 22080 kinds of weather data gathered from 1987 to 1996 for proving the efficiency of this suggested algorithm. Through the experiments, the early optional composed $26{\times}50{\times}1$ nenral network became the most suitable $26{\times}2{\times}1$ structure through the pruning algorithm suggested, in the optimum neural network $26{\times}2{\times}1$, in the case of the error temperature ${\pm}0.5^{\circ}C$, the average was 33.55%, in the case of ${\pm}1^{\circ}C$, the average was 61.57%, they showed more superior than the average 29.31% and 54.47% of the optional designed structure, also. we can reduce the calculation frequency more than maximum 25 times as compared with the optional sturcture neural network in the calculation frequencies.

• Journal of Korea Water Resources Association
• /
• v.54 no.6
• /
• pp.365-379
• /
• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.81-93
• /
• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• Journal of Korean Society of Forest Science
• /
• v.95 no.4
• /
• pp.381-387
• /
• 2006

This study tried to develop a model which can predict a long-term of forest recreation quantity corresponded with econometrics. Simultaneously this study was conducted with the aim of development of practical matrix which is able to apply forest recreation management with policy-control variables about forest supplement with some problem of former study using only a cross-section analysis. As the results of analyses, forest recreation quantity is affected (-) relation by distance, (+) relation by population of the origin area, the size of forest, and a destination's annual social expenditure. In addition, the distance variable is elastic, however, the other variables are inelastic. This results might correspond to a general gravity model theory about forest recreation quantity.

• The Journal of Society for e-Business Studies
• /
• v.26 no.3
• /
• pp.155-174
• /
• 2021

This study noted the emergence of the Resell investment within the fashion market, among emerging investment techniques. Worldwide, the market size is growing rapidly, and currently, there is a craze taking place throughout Korea. Therefore, we would like to use shoe data from StockX, the representative site of Resell, to present basic guidelines to consumers and to break down barriers to entry into the Resell market. Moreover, it showed the current status of the Resell craze, which was based on information from various media outlets, and then presented the current status and research model of the Resell market through prior research. Raw data was collected and analyzed using the XGBoost algorithm and the Prophet model. Analysis showed that the factors that affect the Resell market were identified, and the shoes suitable for the Resell market were also identified. Furthermore, historical data on shoes allowed us to predict future prices, thereby predicting future profitability. Through this study, the market will allow unfamiliar consumers to actively participate in the market with the given information. It also provides a variety of vital information regarding Resell investments, thus. forming a fundamental guideline for the market and further contributing to addressing entry barriers.

• The Korean Journal of Applied Statistics
• /
• v.32 no.6
• /
• pp.795-835
• /
• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.