• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.21-32
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• 2023

The amount of money processed in medicine from the Korea Consumer Agency was studied by the various time series models. The medical data set from the Korea Consumer Agency were consisted of counseling, damage relief and conciliation. For the analysis of time series, autoregressive moving average model, vector autoregressive model and the transfer function model were used. We considered the stationarity and cross correlation function for the identification and fitting. As a result, the transfer function model showed a better prediction. Whereas, the vector autoregressive model also provided good information for the degree and duration of the influence of variables.

• Water Engineering Research
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• v.2 no.4
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• pp.209-218
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• 2001

Generation of evaporation data generally assists in planning, operation, and management of reservoirs and other water works. Annual and monthly evaporation series were generated for King Fahad Dam Lake in Bishah, Saudi Arabia. Data was gathered for period of 22 years. Tests of homogeneity and normality were conducted and results showed that data was homogeneous and normally distributed. For generating annual series, an Autoregressive first order model AR(1) was used and for monthly evaporation series method of fragments was used. Fifty replicates for annual series, and fifty replicates for each month series, each with 22 values length, were generated. Performance of the models was evaluated by comparing the statistical parameters of the generated series with those of the historical data. Annual and monthly models were found to be satisfactory in preserving the statistical parameters of the historical series. About 89% of the tested values of the considered parameters were within the assigned confidence limits

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.264-269
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• 2001

In this paper, we interpret a score as a time series and deal with the fuzzy logic-based modeling of it. The musical notes in a score represent a lot of information about the length of a sound and pitches, etc. In this paper, using melodies, tones and pitches in a score, we transform data on a score into a time series. Once more, we foml the new Lime series by sliding a window through the time series. For analyzing the time series data, we make use of the Box-Jenkins s time series analysis. On the basis of the identified characteristics of time series, we construct the fuzzy model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.211-214
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• 2001

In this paper, we interpret a score as a time series and deal with the fuzzy logic-based modeling of it. The musical notes in a score represent a lot of information about the length of a sound and pitches, etc. In this paper, using melodies, tones and pitches in a score, we transform data on a score into a time series. Once more, we form the new time series by sliding a window through the time series. For analyzing the time series data, we make use of the Box-Jenkinss time series analysis. On the basis of the identified characteristics of time series, we construct the fuzz model.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.675-683
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• 2015

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

• Journal of Environmental Health Sciences
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• v.17 no.2
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• pp.1-8
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• 1991

The stochastic variance and structures of time series data on air quality were examined by employing the techniques of autocorrelation function, variance spectrum, fourier series, ARIMA model. Among the air quality properties of atmosphere, SO$_{2}$ is one of the most siginificant and widely measured parameters. In the study, the air quality data were included hourly observations on SO$_{2}$ TSP and O$_{3}$. The data were measured by automatic recording instrument installed in Kwanghwamoon during February and March in 1991. The results of study were as follows 1. Hourly air quality series varied with the domiant 24 hour periodicity and the 12 hour periodic variation was also observed. 2. The correlation coefficients between SO$_{2}$ and O$_{3}$ is -0.4735. 3. In simulating or forecasting variation in SO$_{2}$ ARIMA models are on a useful tools. The multiplicative seasonal ARIMA (1, 1, 0) (0, 2, 1)$_{24}$ model provided satisfactory results for hourly SO$_{2}$ time series.

• Journal of Preventive Medicine and Public Health
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• v.22 no.2 s.26
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• pp.189-196
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• 1989

In January 1988, district medical insurance plan was executed on a national scale in Korea. We conducted an evaluation of the impact of execution of district medical insurance plan on number of hospital patients: number of outpatients; and occupancy rate. This study was carried out by Box-Jenkins time series analysis. We tested the statistical significance with intervention component added to ARIMA model. Results of our time series analysis showed that district medical insurance plan had a significant effect on the number of outpatients and occupancy rate. Due to this plan the number of outpatients had increased by 925 patients every month which is equivalent to 8.3 percents of average monthly insurance outpatients in 1987, and occupancy rate had also increased by 0.12 which is equivalent to 16 percents of that in 1987.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.365-382
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• 1997

In this paper we study the behaviors of the generalized Durbin-Watson (DW) statistics when the nonstationary seasonal time series regression model is misspecified. It is observed that when the series is seasonally integrated the generalized DW statistic for the seasonal period order autocorrelation converges in probability to zero while teh generalized DW statistic for the first order autocorrelation has nondegenerate asymptotic distribution. When the series is regularly and seasonally integrated the generalized DW for the first order autocorrelation still converges in probability to zero.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.524-524
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• 2000

We have been developing a method to build one-step-ahead prediction models for time series using genetic programming (GP). Our model building method consists of two stages. In the first stage, functional forms of the models are inherited from their parent models through crossover operation of GP. In the second stage, the parameters of the newborn model arc optimized based on an iterative method just like the back propagation. The proposed method has been applied to various kinds of time series problems. An application to the seismic ground motion was presented in the KACC'99, and since then the method has been improved in many aspects, for example, additions of new node functions, improvements of the node functions, and new exploitations of many kinds of mutation operators. The new ideas and trials enhance the ability to generate effective and complicated models and reduce CPU time. Today, we will present a couple of financial applications, espc:cially focusing on gold price prediction in Tokyo market.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.537-543
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• 2007

Since the GPS absolute positioning with pseudorange measurements can significantly be affected by the observation error, the time series analysis of the GPS receiver clock errors was performed in this study. From the estimated receiver clock errors, the time series model is generated, and constrained back in the absolute positioning process. One of the CORS (Continuously Operating Reference Stations) network is used to analyze the behavior of the receiver clock. The dominant part of the model is the linear trend during 24 hours, and the seasonal component is also estimated. After constraining the modeled receiver clock errors, the estimated position error compared to the published coordinates is improved from ${\pm}11.4\;m\;to\;{\pm}9.5\;m$ in 3D RMS.