• Ocean and Polar Research
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• v.36 no.4
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• pp.437-445
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• 2014

A time-series sediment trap was operated at a water depth of 4950 m from July 2003 to May 2004 at KOMO station ($10^{\circ}30^{\prime}N$, $131^{\circ}20^{\prime}W$) in the northeastern equatorial Pacific, with the aim of understanding the temporal variation of planktonic foraminifera assemblages in response to the seasonal shift of Inter-Tropical Convergence Zone (ITCZ). A total of 22130 planktonic foraminifera specimens belonging to 30 species and 11 genera were identified, which shows a distinct seasonal variation with high values (125~288 specimens $m^{-2}day^{-1}$) in the winter to spring (December-May) and low values (16~23 specimens $m^{-2}day^{-1}$) in the fall (September-November). In addition, seasonal ecological differences of foraminifera assemblages are distinctly recognizable: omnivorous foraminifera occurred predominantly during the summer season, whereas herbivorous ones were dominant during the winter season. Such seasonal variations correspond to the seasonal shift of the ITCZ. Enhanced occurrence of herbivorous species during the winter-spring season seems a result of surface water mixing generated by the southward shift of the ITCZ. The increase in omnivorous species during the summer season may be due to the northward movement of the ITCZ caused by weakened wind speed, resulting in the intensification of water column stratification and nutrient-poor environment. A significant reduction of planktonic foraminifera specimens during the fall is attributed to heavy precipitation and reduction in light intensity.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.117-124
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• 2009

If time series data with seasonal effect exist, various statistical models like winters for successful forecasts could be used. But if the data are not enough to estimate seasonal effect, not much methods are available. This paper proposes the statistical forecasting method based on cumulative data when the data are not enough to estimate seasonal effect. We apply this method to real cosmetic sales data and show its better performance over moving average method.

• Ocean and Polar Research
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• v.30 no.1
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• pp.33-46
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• 2008

In order to investigate seasonal variations of the physical environments in the region of Jinhae Bay-Nakdongpo, we carried out hydrographic surveys from November 2000 to November 2001. Horizontal and vertical distribution of salinity and temperature shows large seasonal variations. Water column is well mixed in winter and stratified in summer. Low-salinity water is distributed in the form of patches because of the drainage control at the Nakdong River. Seasonal variations in the sea near Gadeok-Sudo are affected by topography, river discharge and tidal current. Currents have been measured using a bottom mounted ADCP and DCM12 between November 2000 and August 2001 in the Gadeok-Sudo. The current in the Gadeok-Sudo shows a distinct two-layer structure with reversed current. Low-pass filtered time series of wind, sea elevation and current are coherent for the period of 1-2 days and are attributed to Ekman-like dynamics. Spatial and temporal circulation pattern shows a slight different. The subtidal current in Jinhae Bay goes northward, however is reversed in the Gadeok-Sudo mouth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3381-3388
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• 2012

This research is to find out the method appropriate for the forecasting of passennger demand using seasonal ARIMA model and efficient operation in Jeju National Airport. Time series monthly data for the investigation were collected ranging from January 2003 to December 2011. A total of 108 observations were used for data analysis. Research findings showed that the multiplicative seasonal ARIMA(0.1.2)(0.1.1)12 model is appropriate model. The number of passengers in Jeju National Airport will continue to rise, it was expected to surpass 20 million people.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.65-73
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• 2001

Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.

• Korean Management Science Review
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• v.32 no.2
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• pp.69-78
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• 2015

This paper considers a Bayesian Poisson model for multivariate count data using multiplicative rates. More specifically we compose the parameter for overall arrival rates by the product of two parameters, a common effect and an individual effect. The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series. In addition, analysis on individual effects allows the researcher to differentiate the time series by whatevercharacterization of their choice. This type of model allows the researcher to specifically analyze two different forms of effects separately and produce a more robust result. We illustrate a simple MCMC generation combined with a Gibbs sampler step in estimating the posterior joint distribution of all parameters in the model. On the whole, the model presented in this study is an intuitive model which may handle complicated problems, and we highlight the properties and possible applications of the model with an example, analyzing real time series data involving customer arrivals to a large retail store.

• Journal of Medicine and Life Science
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• v.16 no.3
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• pp.90-95
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• 2019

The Korea Centers for Diseases Control and Prevention interpreted that recent outbreaks of scarlet fever in Korea since 2011 was resulted from the expansion of scarlet fever notification criteria. To suggest a relevant hypothesis regarding this emerging outbreak, a time series analysis(TSA) of scarlet fever incidence between 2002 and 2016 was conducted. The raw data was the nationwide insurance claims database administered by the Korean National Health Insurance Service. The inclusion criteria were children aged ≤14 years residing in Jeju Province, Korea who received any form of healthcare for scarlet fever from 2002 to 2016. The season was defined as winter (December, January, February; Q1), spring (March, April, May; Q2), summer (June, July, August; Q3), and autumn (September, October, November; Q4). There were seasonal variations with showing peak season on Q1 and Q3. And three phases as 2002 Q2~2005 Q2, 2005 Q2~2009 Q4, and 2010 Q1~2016 Q4 were found between 2002 and 2016. The results from TSA suggested that the recent outbreak of scarlet fever among children in Jeju Province might be a phenomenon from 'unknown birth-related environmental factors' changed after 2010.

• Geophysics and Geophysical Exploration
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• v.19 no.2
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• pp.91-96
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• 2016

From the Earth's polar motion time series (IERS 08 C04, since 1981), after removal of seasonal variation by band-pass filtering, we acquired Earth's free Eulerian motion (Chandler wobble) time series. By successive least square error fittings on it, we analyzed amplitude and phase variation of Chandler wobble. We attempted to identify any precursory behavior of the pole before large earthquakes but only to fail. Unlike Smylie's conjecture there was no appreciable motion of the Earth's pole detected at around the each times of recent six largest earthquakes of magnitude over 8.5.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.313-319
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• 2021

The national geodetic reference frame of Korea was adopted in 2003, which is referenced to ITRF (International Terrestrial Reference Frame) 2000 at the epoch of January 1, 2002. For precise positioning based on the satellites, it should be thoroughly maintained to the newest global reference frame. Other than plate tectonic motion, there are significant events or changes such as earthquakes, antenna replacement, PSD (Post-Seismic Deformation), seasonal variation etc. We processed three years of GNSS (Global Navigation Satellite System) data(60 NGII CORS stations, 51 IGS core stations) to produce daily solutions minimally constrained to ITRF. From the time series of daily solutions, the sites with unexpected discontinuity were identified to set up an event(mostly antenna replacement). The combined solution with minimum constraints was estimated along with the velocity, the offsets, and the periodic signals. The residuals show that the surrounding environment also affects the time series to a certain degree, thus it should be improved eventually. The transformation parameters to ITRF2014 were calculated with stability and consistency, which means the national geodetic reference frame is properly aligned to the global reference frame.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.