• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.829-843
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• 2010

To test for the serial dependence in time series of counts data, Jung and Tremayne (2003) evaluated the size and power of several tests under the class of INARMA models based on binomial thinning operations for Poisson marginal distributions. The overdispersion phenomenon(i.e., a variance greater than the expectation) is common in the real world. Overdispersed count data can be modeled by using alternative thinning operations such as random coefficient thinning, iterated thinning, and quasi-binomial thinning. Such thinning operations can lead to time series models of counts with negative binomial or generalized Poisson marginal distributions. This paper examines whether the test statistics used by Jung and Tremayne (2003) on serial dependence in time series of counts data are affected by overdispersion.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.353-371
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• 2022

This paper analyzes death counts after World War II of several countries to identify and to compare their stochastic structures. The stochastic structures that this paper entertains are three structural time series models, a local level with a random walk model, a fixed local linear trend model and a local linear trend model. The structural time series models assume that a time series can be formulated directly with the unobserved components such as trend, slope, seasonal, cycle and daily effect. Random effect of each unobserved component is characterized by its own stochastic structure and a distribution of its irregular component. The structural time series models use the Kalman filter to estimate unknown parameters of a stochastic model, to predict future data, and to do filtering data. This paper identifies the best-fitted stochastic model for three types of death counts (Female, Male and Total) of each country. Two diagnostic procedures are used to check the validity of fitted models. Three criteria, AIC, BIC and SSPE are used to select the best-fitted valid stochastic model for each type of death counts of each country.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.103-125
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• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.181-193
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• 2017

The paper introduces bottom-up and optimal combination methods that can analyze and forecast hierarchical time series. These methods allow forecasts at lower levels to be summed consistently to upper levels without any ad-hoc adjustment. They can also potentially improve forecast performance in comparison to independent forecasts. We forecast regional traffic accident counts as time series data in order to identify efficiency gains from hierarchical forecasting. We observe that bottom-up or optimal combination methods are superior to independent methods in terms of forecast accuracy.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.487-500
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• 2020

Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we present that the firms' volatilities are correlated between some they belong to and that such a clustering effect remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500 that have their own volatility clustering.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.229-239
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• 2018

This paper introduces how to adopt the concept of temporal hierarchies to forecast time series data. Similarly as in hierarchical cross-sectional data, temporal hierarchies can be constructed for any time series data by means of non-overlapping temporal aggregation. Reconciliation forecasts with temporal hierarchies result in more accurate and robust forecasts when compared with the independent base and bottom-up forecasts. As an empirical example, we forecast traffic accident counts with temporal hierarchies and observe that reconciliation forecasts are superior to the base and bottom-up forecasts in terms of forecast accuracy.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.380-388
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• 2014

Annual average daily traffic(AADT) serves as important basic data in the transportation sector. AADT is used as design traffic which is the basic traffic volume in transportation planning. Despite of its importance, at most locations, AADT is estimated using short term traffic counts. An accurate AADT is calculated through permanent traffic counts at limited locations. This study dealt with estimating AADT using various models considering both the spatial correlation and time series data. Kriging models which are commonly used spatial statistics methods were applied and compared with each model. Additionally the External Universal kriging model, which includes explanatory variables, was used to assure accuracy of AADT estimation. For evaluation of various kriging methods, AADT estimation error, proposed using national highway permanent traffic count data, was analyzed and their performances were compared. The result shows the accuracy enhancement of the AADT estimation.

• 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 Korean Data and Information Science Society
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• v.26 no.6
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• pp.1565-1572
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• 2015

In this paper, we analyze the polio incidence data based on the Poisson autoregressive models, focusing particularly on change-point detection. Since the data include some strongly deviating observations, we employ the robust cumulative sum (CUSUM) test proposed by Kang and Song (2015) to perform the test for parameter change. Contrary to the result of Kang and Lee (2014), our data analysis indicates that there is no significant change in the case of the CUSUM test with strong robustness and the same result is obtained after ridding the polio data of outliers. We additionally consider the comparison of the forecasting performance. All the results demonstrate that the robust CUSUM test performs adequately in the presence of seemingly outliers.

• 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.