• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.835-844
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• 2010

Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.163-167
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• 2003

Based on the transient finite difference solution of Richards' equation, an infiltration model is developed to analyze temporal variation of precipitation recharge in the region of monsoon climates. Simulation results obtained by using time series data of 20-year daily precipitation and pan evaporation indicate that a linear relationship between the annual precipitation and the annual recharge holds for the soils under the monsoon climates with varying degrees of the correlation coefficient depending on the soil types. A sensitivity analysis reveals that the water table depth has little effects on the recharge for the sandy soil, whereas, for the loamy and silty soils, rise of the water table at shallow depths causes increase of evaporation by approximately 100㎜/yr and a corresponding decrease in recharge. A series of simulations for two-layered soils illustrate that the amount of recharge is dominantly determined by the soil properties of the upper layer, although the temporal variation of recharge is affected by both layers.

• Journal of Oriental Neuropsychiatry
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• v.22 no.4
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• pp.111-124
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• 2011

Objectives : This case series was conducted to report the efficacy of Ondam-tanggami for insomnia. Methods : Insomnia patients with more than 15 points on Insomnia Severity Index scale were assessed using SCL-90-R, STAI, STAXI, BDI. Being treated with Ondam-tanggami after 2 weeks, ISI, STAI, STAXI, BDI were re-measured to determine the progress of insomnia. It is measured that total sleep time, number of awaking times during sleep, satisfaction of sleep daily. Results : After treatment, quality of sleep has improved and ISI, STAI, STAXI, BDI score have decreased. Conclusions : According to the study, treatment with Ondam-tanggami for insomnia has shown positive results. Further use of Ondam-tanggami is much anticipated for future treatment of insomnia cases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.35-37
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• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.

• Journal of Preventive Medicine and Public Health
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• v.34 no.1
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• pp.61-72
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• 2001

Objectives : To evaluate the hypothesis that increasing ambient levels of ozone or particulate matter are associated with increased emergency room visits for asthma and to quantify the strength of association, if any, between these. Methods : Daily counts of emergency room visits for asthma, air quality, and weather data were collected from hospitals with over 200 beds and from monitoring Stations in Seoul, Korea from 1994 through 1997. Daily counts of emergency mom visits for asthma attack were analyzed using a general additive Poisson model, with adjustment for the effects of secular trend, seasonal variation, Sunday and holiday, temperature, and humidly, according to levels of ozone and particulate matter. Results : The association between daily counts of emergency room visits for asthma attack and ozone levels was statistically significant in summer(from June to August), and the RR by unit inclement of 100 ppb ozone was 1.30(95% CI = $1.11\sim1.52$) without lag time. With restriction of the period from April to September in 1996, the RR was 1.37(95% CI = $1.06\sim1.76$), and from June to August in 1995, the RR was 1.62(95% CI = $1.12\sim2.35$). In the data for children$(5\sim14yr)$, the RR was 2.57(95% CI = $1.31\sim5.05$) with restriction of the period from April to September in 1997. There was no Significant association between TSP levels and asthma attacks, but a slight association was seen between PM10 levels and asthma attacks in a very restricted period. Conclusion : There was a statistically significant association between ambient levels of ozone and daily counts of emergency room visits for asthma attack. Therefore, we must make efforts to effectively minimize air pollution, in order to protect public health.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.83-92
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• 2021

There are continuous and tremendous attempts to apply various sensor systems and ICTs into the dairy science for data accumulation and improvement of dairy productivity. However, these only concerns the fields which directly affect to the dairy productivity such as the number of individuals and the milk production amount, while researches on the physiology aspects of dairy cows are not enough which are fundamentally involved in the dairy productivity. This paper proposes the basic approach for extraction of daily behavior pattern from hourly behavioral data of dairy cows to identify the health status and stress. Total four clusters were grouped by k-means clustering and the reasonability was proved by visualization of the data in each groups and the representatives of each groups. We hope that provided results should lead to the further researches on catching abnormalities and disease signs of dairy cows.

• Journal of Environmental Health Sciences
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• v.38 no.3
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• pp.184-194
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• 2012

Objectives: We investigated the effects of air pollution on allergic diseases (allergic rhinitis, asthma, atopic dermatitis) in metropolitan cities in Korea, adjusting for meteorological factors. Methods: Data on daily hospital visits and hospital admissions for 2003-2010 was obtained from the National Health Insurance Cooperation. Meteorological data was obtained from the Korea Meteorological Administration. We then calculated daily mean temperature, daily mean humidity, daily mean air pressure at sea level, and diurnal temperature range. We used data on air pollution provided by the National Institute of Environmental Research. Maximum daily eight-hour average ozone concentrations and the daily mean $PM_{10}$ were used. We estimated excess risk and 95% confidence interval for the increasing interquatile range (IQR) of each air pollutant using Generalized Additive Models (GAM) that appropriate for time series analysis. Results: In this study, we observed an association between ozone and hospital visits for allergic rhinitis, asthma, and atopic dermatitis in all metropolitan cities, adjusting for temperature, humidity, air pressure at sea level, diurnal temperature range, and day of the week. Ozone was associated with hospital visits for allergic rhinitis, asthma, and atopic dermatitis across all metropolitan cities. However $PM_{10}$ was associated with allergic-related diseases in only select cities. Also, ozone and $PM_{10}$ were associated with hospital admission for asthma in all cities except Gwangju. Hospitalization for the other diseases failed to show consistent association with air pollutants. Conclusion: In the findings of this study, there was a significant association between air pollutants and allergic-related diseases. More detailed research subdivided age group or conducting meta-analyses combining data of all cities is required.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.1-9
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• 2021

This paper investigates the impact of the domestic and global outbreak of the coronavirus (COVID-19) pandemic on the trading size of the Malaysian stock (MS) market. The theoretical model posits that stock markets are affected by their response to disasters and events that arise in the international or local environments, as well as to several financial factors such as stock volatility and spread bid-ask prices. Using daily time-series data from 27 January to 12 May 2020, this paper utilizes the traditional Augmented Dickey and Fuller (ADF) technique and Zivot and Andrews with structural break' procedures for a stationarity test analysis, while the autoregressive distributed lag (ARDL) method is applied according to the trading size of the MS market model. The analysis considered almost all 789 listed companies investing in the main stock market of Malaysia. The results confirmed our hypotheses that both the daily growth in the active domestic and global cases of coronavirus (COVID-19) has significant negative effects on the daily trading size of the stock market in Malaysia. Although the COVID-19 has a negative effect on the Malaysian stock market, the findings of this study suggest that the COVID-19 pandemic may have an asymmetric effect on the market.