• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.31-39
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• 2020

In this paper, we introduce a technique for generating synthetic benchmarks based on time series data. Many of the data measured on IoT devices have a time series characteristic that measures numerical changes over time. However, there is a problem that it is difficult to model the data measured over a long period as generalized time series data. To solve this problem, this paper introduces the BST-IGT model. The BST-IGT model separates the entire data into sections that can be easily time-series modeled, collects the generated data into templates, and produces new synthetic benchmarks that share or modify characteristics based on them. As a result of making a new benchmark using the proposed modeling method, we could create a benchmark with multiple aspects by mixing the composite benchmark with the statistical features of the existing data and other benchmarks.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.153-166
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• 2022

Structural pounding due to strong seismic excitations can result in severe damage or even collapse of colliding structures. Many researchers focused on studying the mutual pounding between two adjacent structures while very few researches were concerned with the pounding of a series of structures. This paper aims to study the pounding effect on a series of four buildings having different natural frequencies. The paper also investigates the effect of different arrangements of the four buildings on their pounding response. For this, a mathematical model was constructed using Matlab code where, pounding was modeled using a contact force-based approach. A Non-Linear viscoelastic (Hertzdamp) contact element was used and activated only during the approach period of collision. The mathematical model was validated by comparing its prediction versus experimental results on three adjacent buildings. Then the model was used to study the pounding between four adjacent structures arranged in different sequences according to their natural frequencies. The results revealed that increasing the gap distance generally led to decrease the peak responses of the towers. Such response is somehow different from that predicted earlier by the authors for the case of three adjacent buildings. Moreover, the arrangement of towers has a significant effect on their pounding response. Significant difference between the natural frequencies of adjacent structures increases the pounding forces especially when the more flexible buildings are located at the outer edge of the series. The study points out the need for further researches on buildings in series to gain a better understanding of such complex phenomena.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.44-51
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• 2008

Pattern matching and pattern searching in time series data have been active issues in a number of disciplines. This paper suggests a novel pattern matching technology which can be used in the field of stock market analysis as well as in forecasting stock market trend. First, we define conceptual patterns, and extract data forming each pattern from given time series, and then generate learning model using Hidden Markov Model. The results show that the context-based pattern matching makes the matching more accountable and the method would be effectively used in real world applications. This is because the pattern for new data sequence carries not only the matching itself but also a given context in which the data implies.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.517-530
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• 2008

In this paper we introduce a time series model using the triangle fuzzy numbers in order to construct a statistical relation for the data which is a sequence of observations which are ordered in time. To estimate the proposed fuzzy model we split of a universal set includes all observation into closed intervals and determine a number and length of the closed interval by the frequency of events belong to the interval. Also we forecast the data by using a difference between observations when the fuzzified numbers equal at successive times. To investigate the efficiency of the proposed model we compare the ordinal and the fuzzy time series model using examples.

• Journal of Power Electronics
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• v.19 no.2
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• pp.519-528
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• 2019

The unified power quality conditioner (UPQC) is an effective custom power device that is used at the point of common coupling to protect loads from voltage and current-related PQ issues. Currently, most researchers have studied series unit and parallel unit models and an idealized transformer model. However, the interactions of the series and parallel converters in AC-link are difficult to analyze. This study utilizes an equivalent transformer model to accomplish an electric connection of series and parallel converters in the AC-link and to establishes a precise unified mathematical model of the UPQC. The strong coupling interactions of series and parallel units are analyzed, and they show a remarkable dependence on the excitation impedance of transformers. Afterward, a feed-forward decoupling method based on a unified model that contains the uncertainty components of the load impedance is applied. Thus, this study presents an adaptive method to estimate load impedance. Furthermore, simulation and experimental results verify the accuracy of the proposed modeling and decoupling algorithm.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.261-266
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• 2014

When the number of parameters in the time series model are diverse, it is hard to forecast because of the increasing error by a parameter estimation. If the homogeneity hypothesis which was obtained from the same model about severeal data for the time series is selected, it is easy to get the predictive value better. Nonlinear time-series panel data for each parameter for each time series, since there are so many parameters that are present, and the large number of parameters according to the parameter estimation error increases the accuracy of the forecast deteriorated. Panel present in the time series of multiple independent homogeneity is satisfied by a comprehensive time series to estimate and to test of the parameters. For studying about the homogeneity test for the m independent non-linear of the time series panel data, it needs to set the model and to make the normal conditions for the model, and to derive the homogeneity test statistic. Finally, it shows to obtain the limit distribution according to ${\chi}^2$ distribution. In actual analysis,, we can examine the result for the homogeneity test about nonlinear time series panel data which are 2 groups of stock price data.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.51-63
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• 2008

Jeju-Mainland demand for air passenger is variated by the season because most of the demands stem from the leisure travel. This research is to estimate the econometrics demand models(A simple time series model and the partial adjustment model) and elasticities of each models for the Jeju-Mainland domestic routes air travel market using the time series aggregate data between the year 1996 and 2005. As the result of estimating, income elasticity was evaluated to be elastic(1.55) and fare elasticity was inelastic(-0.49${\sim}$-0.59) for A simple time series models. In the partial adjustment model's case, income elasticity was evaluated to be inelastic(0.51) in short-run whereas it was evaluated to be elastic(1.88) in long-run. Fare elasticity was evaluated to be inelastic in short-run(high-demand season: -0.13, slack season: -0.20) and long-run(high-demand season: -0.48, slack season: -0.72).

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1019-1032
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• 2013

The accurate prediction of future mortality is an important issue due to recent rapid increases in life expectancy. An accurate estimation and prediction of mortality is important to future welfare policies. The optimal selection of a mortality model is important to estimate and predict mortality; however, the period of time series data used is also an important issue. It is essential to understand that the time series data for mortality is short in Korea and the data before 1982 is incomplete. This paper divides the time series of Korean mortality into two sets to compare the parameter estimates of the LC model and LC model with a cohort effect by the period of data used. A modeling and prediction of the mortality index and cohort effect index as well as the evaluation of future life expectancy is conducted. Finally, some suggestions are proposed for the future prediction of mortality.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.1017-1028
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• 2009

NSRPM (Neyman-Scott Rectangular Pulse Model) is one of the common model for generating future precipitation time series in stochastical hydrology. There are 5 parameters to compose the NSRPM model for generating precipitation time series. Generally parameter estimation using moment has some problems related with increased objective functions and shows different results in accordance with random variable generating models. In this study, direct parameter estimation method was proposed to cover with disadvantages of parameter estimation using moment. To apply the direct parameter estimation, generating stochastical data variance in accordance with numbers of precipitation events of NSRPM was done. Both kinds of methods were applied at the Cheongju gauge station data. Precipitation time series were generated using 4 different random variable generator, and compared with observed time series to check the accuracies. As a results, direct method showed more stable and better results.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.