• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.108-119
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• 2024

Objectives: Abrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous. Methods: This time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand's Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels. Results: Before the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 ㎛ or less (PM_2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 ㎍/m³ increase in PM_2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM_2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM_2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak. Conclusions: Lockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM_2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM_2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.671-679
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• 2000

Regionalization technique using flow duration curve and spatial interpolation algorithm is developed for the purpose of estimating daily flow time series at ungauged station. In this study, we assumed a part of 8 gauging stations of Nakdong River basin as ungauged stations. Then, we generated flow duration curves and daily flow hydrographs by regionalization technique at ungauged stations. And we compared generated and observed hydrographs. The simulation results showed that the observed flows were well simulated by the proposed method and that the general patterns of the observed flows were satisfactorily reproduced by the regionalization technique. From these results, it is possible that we obtain daily flow information without application of labour intensive and time consuming deterministic models, which require complicating quantification of model parameter values. And we compared the regionalization techniques with the specific discharge method which is the most general approach in hydrological practice in Korea. The results showed that the regionalization technique was superior to specific discharge method.method.

• Journal of Korean Society of Disaster and Security
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• v.8 no.1
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• pp.21-27
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• 2015

Recent researches show that climate change has impact on the rainfall process at different temporal and spatial scales. The present paper is focused on climate change impact on sub-daily rainfall quantile of Han River basin in South Korea. Climate change simulation outputs from ECHO-G GCM under the A2 scenario were used to estimate daily extreme rainfall. Sub-daily extreme rainfall was estimated using the scale invariance concept. In order to assess sub-daily extreme rainfall from climate change simulation outputs, precipitation time series were generated based on NSRPM (Neyman-Scott Rectangular Pulse Model) and modified using the ratio of rainfall over projection periods to historical one. Sub-daily extreme rainfall was then estimated from those series. It was found that sub-daily extreme rainfall in the future displayed increasing or decreasing trends for estimation methods and different periods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.271-274
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• 1998

In this paper we examine long-term memory of the financial time-series by employing the R/S analysis, the Hurst exponent estimation, and the modified R/S analysis. The null hypothesis of white-noise is tested using the NYSE daily indexes from January 1966 to July 1998, and the results show that long-range dependence exists before the apparent structural break of the Black Monday in 1987.

• The Korean Journal of Financial Studies
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• v.6 no.1
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• pp.335-356
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• 2000

This paper examines the time series dynamics of spot and forward exchange rates and Eurocurrency deposit rates for four bilateral relationships vis a vis the U.S. dollar using daily data. The equilibrium implied by covered interest parity provides a theoretical foundation from which to estimate and analyze the dynamic properties of each system of exchange rates and interest rates. The structural statistical model is identified by relying on the implied cointegration vectors and long-run neutrality restrictions.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.113-129
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• 2023

Purpose - The purpose of this study is to examine whether trading volume amplifies the extent to which lottery-type stocks are overpriced, and whether economic sentiment index explains time-variation in the magnitude of the volume amplification effect. Design/methodology/approach - We examine monthly returns on 5x5 monthly bivariate portfolios formed by lottery characteristics (measured by maximum daily return) and trading volume. In addition, we perform time-series regression tests to examine how the volume amplification effect changes in high and low economic sentiment periods, after controlling for Fama-French three factors. Findings - Our bivariate portfolio analysis shows that the overpricing of lottery-type stocks are mostly pronounced among high trading volume stocks. In contrast, for low trading volume stocks, overpricing of lottery-type stocks appears to vanish. Furthermore, the amplification effect of trading volume on overpricing of lottery-type stock is concentrated in high economic sentiment periods. Research implications or Originality - This study is the first attempt to examine whether trading volume drives lottery-type stocks' overpricing in the Korean stock market. Furthermore, our analysis unveils the time-varying nature of volume amplification effect. The results suggest that trading volume might play a important hidden role in asset pricing, opening a new line of researches in the future.

• Journal of Preventive Medicine and Public Health
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• v.38 no.3
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• pp.283-290
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• 2005

Objectives : The aim of this paper was to examine the relationship between the summertime (June to August) heat index, which quantifies the bioclimatic apparent temperature in sultry weather, and the daily disease-related mortality in Seoul for the period from 1991 to 2000. Methods : The daily maximum (or minimum) summertime heat indices, which show synergetic apparent temperatures, were calculated from the six hourly temperatures and real time humidity data for Seoul from 1991 to 2000. The disease-related daily mortality was extracted with respect to types of disease, age and sex, etc. and compared with the time series of the daily heat indices. Results : The summertime mortality in 1994 exceeded the normal by 626 persons. Specifically, blood circulation-related and cancer-related mortalities increased in 1994 by 29.7% (224 persons) and 15.4% (107 persons), respectively, compared with those in 1993. Elderly persons, those above 65 years, were shown to be highly susceptible to strong heat waves, whereas the other age and sex-based groups showed no significant difference in mortality. In particular, a heat wave episode on the 22nd of July 2004 ($>45^{\circ}C$ daily heat index) resulted in double the normal number of mortalities after a lag time of 3 days. Specifically, blood circulation-related mortalities, such as cerebral infraction, were predominant causes. Overall, a critical mortality threshold was reached when the heat index exceeded approximately $37^{\circ}C$, which corresponds to human body temperature. A linear regression model based on the heat indices above $37^{\circ}C$, with a 3 day lag time, accounted for 63% of the abnormally increased mortality (${\geq}+2$ standard deviations). Conclusions : This study revealed that elderly persons, those over 65 years old, are more vulnerable to mortality due to abnormal heat waves in Seoul, Korea. When the daily maximum heat index exceeds approximately $37^{\circ}C$, blood circulation-related mortality significantly increases. A linear regression model, with respect to lag-time, showed that the heat index based on a human model is a more dependable indicator for the prediction of hot weather-related mortality than the ambient air temperature.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.54-61
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• 2019

Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver's behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Journal of Korean Society of Water and Wastewater
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• v.10 no.2
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• pp.88-103
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• 1996

The objective of this study is to develop stochastic and deterministic models which could be used to synthesize water application time series. Adaptive models using mulitivariate ARIMA(Transfer Function Model) are developed for daily urban water use forecasting. The model considers several variables on which water demands is dependent. The dynamic response of water demands to several factors(e.g. weekday, average temperature, minimum temperature, maximum temperature, humidity, cloudiness, rainfall) are characterized in the model by transfer functions. Daily water use data of Kumi city in 1992 are employed for model parameter estimation. Meteorological data of Seonsan station are utilized to input variables because Kumi has no records about the meteorological factor data.To determine the main factors influencing water use, autocorrelogram and cross correlogram analysis are performed. Through the identification, parameter estimation, and diagnostic checking of tentative model, final transfer function models by each month are established. The simulation output by transfer function models are compared to a historical data and shows the good agreement.