• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.95-104
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• 2020

In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to non-stationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea's stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market's volatility than the positive shock in the case of South Korea and Tokyo.

• Journal of agriculture & life science
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• v.45 no.5
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• pp.115-126
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• 2011

This paper estimates the number of Korean cattle using time-series ARIMA model. This study classifies the structure of the number of cattle into six indexes to reflect the characteristics of cattle. This study apply ARIMA model to these six indexes according to Box-Jenkins procedure to identify, estimate and predict. The rates of slaughter for aged female and aged male cow is analyzed as non-stationary time series which has unit roots and other 4 indexes is analyzed as stationary time series. The differencing is applied to get rid of non-stationarity for the non-stationary time series. The results show that the number of cattle will be reduced from 2012 as a higher point and rebounded from 2018 as a lower point.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.152-158
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• 2008

In shallow water environment, the detection performance of an active sonar using matched filter with LFM(linear frequency modulation) pulse can be seriously degraded by reverberation which is considered as non-white noise. To reduce the effect of reverberation, a whitening filter preceding the matched fitter, is usually adopted. In the conventional pre-whitening filter, it is assumed that local stationarity is preserved between detection block and its right ahead block. And then by using the characteristics of the reverberation of preceding block, the reverberation of detection block is estimated and whitened. According to the environment of shallow water, the stationarity of reverberation may be preserved for more blocks. In this case, the reverberation of the detection block can be estimated more accurately if more blocks are used. In this paper, the real reverberation signal which is obtained from shallow sea is analyzed and its proper region of estimation block is examined. And the performance of pre-whitening matched filter is compared and analyzed according to the region of estimation block.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.449-457
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• 2011

Recently, increasing heavy rainfalls due to climate change and/or variability result in hydro-climatic disasters being accelerated. To cope with the extreme rainfall events in the future, hydrologic frequency analysis is usually used to estimate design rainfalls in a design target year. The rainfall data series applied to the hydrologic frequency analysis is assumed to be stationary. However, recent observations indicate that the data series might not preserve the statistical properties of rainfall in the future. This study incorporated the residual analysis and the hydrologic frequency analysis to estimate design rainfalls in a design target year considering the non-stationarity of rainfall. The residual time series were generated using a linear regression line constructed from the observations. After finding the proper probability density function for the residuals, considering the increasing or decreasing trend, rainfalls quantiles were estimated corresponding to specific design return periods in a design target year. The results from applying the method to 14 gauging stations indicate that the proposed method provides appropriate design rainfalls and reduces the prediction errors compared with the conventional rainfall frequency analysis which assumes that the rainfall data are stationary.

• Journal of the Korean Statistical Society
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• v.35 no.4
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• pp.355-376
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• 2006

To identify whether the sequence of observations follows a chain dependent process and whether the chain dependent or repeated observations follow stationary process or not, alternative procedures are suggested in this paper. These test procedures are formulated on the basis of logistic regression model under the likelihood ratio test criterion and applied to the daily rainfall occurrence data of Bangladesh for selected stations. These test procedures indicate that the daily rainfall occurrences follow a chain dependent process, and the different types of transition probabilities and overall transition probabilities of Markov chain for the occurrences of rainfall follow a stationary process in the Mymensingh and Rajshahi areas, and non-stationary process in the Chittagong, Faridpur and Satkhira areas.

• Journal of the Korean Mathematical Society
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• v.51 no.3
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• pp.609-633
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• 2014

From the ordinary notion of uniformly strong mixing for a sequence of random variables, a new concept called conditionally uniformly strong mixing is proposed and the relation between uniformly strong mixing and conditionally uniformly strong mixing is answered by examples, that is, uniformly strong mixing neither implies nor is implied by conditionally uniformly strong mixing. A couple of equivalent definitions and some of basic properties of conditionally uniformly strong mixing random variables are derived, and several conditional covariance inequalities are obtained. By means of these properties and conditional covariance inequalities, a conditional central limit theorem stated in terms of conditional characteristic functions is established, which is a conditional version of the earlier result under the non-conditional case.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.

• Steel and Composite Structures
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• v.6 no.4
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• pp.319-333
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• 2006

A rectangular plate made of a porous material is the subject of the work. Its mechanical properties vary continuously on the thickness of a plate. A mathematical model of this plate, which bases on nonlinear displacement functions taking into account shearing deformations, is presented. The assumed displacement field, linear geometrical and physical relationships permit to describe the total potential energy of a plate. Using the principle of stationarity of the total potential energy the set of five equilibrium equations for transversely and in-plane loaded plates is obtained. The derived equations are used for solving a problem of a bending simply supported plate loaded with transverse pressure. Moreover, the critical load of a bi-axially in-plane compressed plate is found. In both cases influence of parameters on obtained solutions such as a porosity coefficient or thickness ratio is analysed. In order to compare analytical results a finite element model of a porous plate is built using system ANSYS. Obtained numerical results are in agreement with analytical ones.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.345-345
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• 2012

정상성 기반의 전통적 극한치 이론은 기후변화 및 변동에 의한 외부변화 요인을 반영하기에는 한계가 있음이 지적되어왔다. 따라서 강우의 빈도분석 시 매개변수의 시간에 따른 변화를 반영한 비정상성 빈도분석 방법이 필요하다. 본 연구에서는 미호천 유역의 강우관측소 중 기상청에서 관리하는 청주 관측소 및 국토해양부에서 관리하는 가덕, 병천, 증평, 진천 관측소의 24시간 연최대치 강우자료를 대상으로 시간에 따른 경향성 분석을 하였다. 또한 자료의 경향성을 고려하여 비정상성 빈도분석을 하였고 외부상관기상변수로써 ENSO(El Nino Southern Oscillation)를 이용하여 비정상성 빈도분석을 실시하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.435-438
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• 2020

시장예측 문제를 해결하기 위하여 과거부터 꾸준한 연구가 진행되어왔다. 하지만 금융 시계열 데이터에는 분산이 일정하지 않으며 Non-stationarity 등 예측을 하는 것에 있어서 여러 가지 방해 요인이 존재한다. 또한 광범위한 데이터 변수는 기존에 사람이 직접 경험적으로 선택하는 것에 한계가 있기 때문에, 모델이 변수를 자동으로 추출할 수 있어야 한다. 본 논문에서는 여러 가지 금융 시계열 데이터의 문제를 고려하여 타임 스텝 정규화를 제안하며 자동 변수 추출을 위해 LSTM 형태의 오토 인코더 모델을 학습하였으며 LSTM 네트워크를 이용하여 시장 예측하는 모델을 제안한다. 해당 시스템은 실제 주식 거래나 시장 거래를 위하여 온라인 학습이 가능하며 긴 기간을 테스트 구간으로 실험한 결과 미래의 수익률을 예측하는 것에 있어서 우수한 성능을 보였다.