• Journal of Mechanical Science and Technology
• /
• v.18 no.9
• /
• pp.1565-1571
• /
• 2004

Ride characteristics of a vehicle moving on a rough ground with changing travel velocity are analyzed in this paper. The solution is difficult due to the non-stationary characteristics of the problem. Hence a new technique has been proposed to overcome this difficulty. This new technique is employed in the analysis of ride characteristics of a vehicle with changing velocity in the time/frequency domain. It is found that the proposed technique gives successful results in modelling non-stationary responses in terms of a series of stationary responses.

• Proceedings of the KIEE Conference
• /
• 2002.07c
• /
• pp.1871-1873
• /
• 2002

Partial Discharge is radiated in the form of electromagnetic wave from variable sources. It can be taken by UHF antenna and the signal pulse from that has a nonstationary time-series which can be evaluated with several methods. One of them is STFT(short time fourier transform) processed in frequency region. Statistical results using STFT show the possibility being able to discriminate between several PD sources.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.4B
• /
• pp.389-397
• /
• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both Gumbel distribution and trend analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• Journal of Korea Port Economic Association
• /
• v.34 no.3
• /
• pp.183-200
• /
• 2018

This study empirically tests the presence of rational bubbles in the ship prices using time series data from October 1996 to April 2017. To detect the existence of ship prices' rational bubbles, we use integration and cointegration tests, which were proposed by Campbell and Shiller(1987) and Diba and Grossman(1988), for circumventing misspecification of ship price model and applying the bubble test to nonstationary time series. The result of integration test supports existence of tanker price's rational bubble. The co-integration test also shows that drybulk ship and containership prices have been overvalued relative to the market fundamental, drybulk and container freight rates, due to non-stationary rational bubbles. These results provide Korean shipping industry and authorities implications that anticyclical ship investment and long-term and steady fleet capacity expansion policy are needed.

• The Korean Journal of Applied Statistics
• /
• v.29 no.3
• /
• pp.525-537
• /
• 2016

The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.3
• /
• pp.697-707
• /
• 2017

In this paper, we propose a modified GARCH(p, q)-X model which is obtained by adding the exogenous variables to the modified GARCH(p, q) process. Some limiting properties are shown under various stationary and nonstationary exogenous processes which are generated by another process independent of the noise process. The proposed model extends the GARCH(1, 1)-X model studied by Han (2015) to various GARCH(p, q)-type models such as GJR GARCH, asymptotic power GARCH and VGARCH combined with exogenous process. In comparison with GARCH(1, 1)-X, we expect that many stylized facts including long memory property of the financial time series can be explained effectively by modified GARCH(p, q) model combined with proper additional covariate.

• Journal of the Korean Statistical Society
• /
• v.26 no.2
• /
• pp.171-180
• /
• 1997

Effects of order misspecification on statistical behavior of unit root tests are studied. We derive the limiting distributions of the Dickey-Fuller test statistics whose numerators are of the form c .int. W dW + .kappa. where W is a standard Brownian motion on [0, 1] and c is a real number. The term .kappa. is a major consequence of order misspecification and its explict expression is derived. Based on an analysis of .kappa., effects of order misspecification on unit root tests for AR(2), ARMA(1, 1), and AR(3) models are investigated.

• The Korean Journal of Applied Statistics
• /
• v.19 no.2
• /
• pp.319-330
• /
• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• The Korean Journal of Applied Statistics
• /
• v.35 no.1
• /
• pp.49-62
• /
• 2022

Realized volatility is well known to have long memory, strong association with other global financial markets and interdependences among macroeconomic indices such as exchange rate, oil price and interest rates. This paper proposes autoencoder factor-augmented heterogeneous autoregressive (AE-FAHAR) model for realized volatility forecasting. AE-FAHAR incorporates long memory using HAR structure, and exogenous variables into few factors summarized by autoencoder. Autoencoder requires intensive calculation due to its nonlinear structure, however, it is more suitable to summarize complex, possibly nonstationary high-dimensional time series. Our AE-FAHAR model is shown to have smaller out-of-sample forecasting error in empirical analysis. We also discuss pre-training, ensemble in autoencoder to reduce computational cost and estimation errors.

• Communications for Statistical Applications and Methods
• /
• v.16 no.1
• /
• pp.127-135
• /
• 2009

It is necessary to forecast the amount of the maximum electricity demand for stabilizing the flow of electricity. The time series data was collected from the Korea Energy Research between January 2000 and December 2006. The data showed that they had a strong linear trend and seasonal change. Winters seasonal model, ARMA model were used to examine it. Root mean squared prediction error and mean absolute percentage prediction error were a criteria to select the best model. In addition, a nonstationary generalized extreme value distribution with explanatory variables was fitted to forecast the maximum electricity.