• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.547-553
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• 2003

In this paper, we designed the PC-based analyzer of the power spectral density that could estimate the heart rate variability from time series data of R-R interval. The power spectral density estimated that it applied the autoregressive model to the measured electrocardiogram during a short period. Also, the characteristics of the designed analyzer are that it could process of the signal filtering, the generation and recomposition of time series and the feature extraction at the same time. Especially the analyzer reconstructed which applied the lowpass filter of the time series composed by the linear interpolation so as to enhance the signal-to-noise feature. We could estimate the power spectral density that confirmed a variety of power peak with low frequency range and high frequency rang of autonomic nerve by the heart rate variability.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.3
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• pp.36-42
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• 2006

The analytical realization of uncoupled system was introduced in this study using times series and its spectrum analysis. The ARMAX spectra of time series methods were compared with the conventional FFT spectrum. Also, the response of second order system uncoupled was solved using the Runge-Kutta Gill method. In this numerical analysis, the displacement, velocity and acceleration were calculated. The displacement response among them was used for the power spectrum analysis. The ARMAX algorithm in time series was proved to be appropriate for the mode estimation and spectrum analysis. Using the separate response of first and second mode, each modes were calculated separately and the response of mixed modes was also analyzed for the mode estimation using several time series methods.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Spatial Information Research
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• v.20 no.4
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• pp.47-55
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• 2012

Normalized Difference Vegetation Index (NDVI) has been used to measure and monitor plant growth, vegetation cover, and biomass from multispectral satellite data. It is also a valuable index in forest applications, providing forest resource information. In this research, an approach for monitoring forest change using MODIS NDVI time series data is explored. NDVI difference-based approaches for a specific point in time have possible accuracy problems and are lacking in monitoring long-term forest cover change. It means that a multi-time NDVI pattern change needs to be considered. In this study, an efficient methodology to consider long-term NDVI pattern is suggested using a harmonic model. The suggested method reconstructs MODIS NDVI time series data through application of the harmonic model, which corrects missing and erroneous data. Then NDVI pattern is analyzed based on estimated values of the harmonic model. The suggested method was applied to 49 NDVI time series data from Aug. 21, 2009 to Sep. 6, 2011 and its usefulness was shown through an experiment.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.133-144
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• 2016

This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.

• Smart Structures and Systems
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• v.33 no.5
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.19-30
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• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.825-835
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• 2008

Multivariate GARCH(MGARCH) has been useful in financial studies and econometrics for modeling volatilities and correlations between components of multivariate time series. An obvious drawback lies in that the number of parameters increases rapidly with the number of variables involved. This thesis tries to resolve the problem by using dimension reduction technique. We briefly review both factor models for dimension reduction and the MGARCH models including EWMA (Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model). We create meaningful portfolios obtained after reducing dimension through statistical factor models and fundamental factor models and in turn these portfolios are applied to MGARCH. In addition, we compare portfolios by assessing MSE, MAD(Mean absolute deviation) and VaR(Value at Risk). Various financial time series are analyzed for illustration.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.421-427
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• 2013

The dependence between wind speeds in multiple wind sites has a considerable impact on the reliability of power systems containing wind energy. This paper presents a new method to generate dependent wind speed time series (WSTS) based on copulas theory. The basic feature of the method lies in separating multivariate WSTS into dependence structure and univariate time series. The dependence structure is modeled through the use of copulas, which, unlike the cross-correlation matrix, give a complete description of the joint distribution. An autoregressive moving average (ARMA) model is applied to represent univariate time series of wind speed. The proposed model is illustrated using wind data from two sites in Canada. The IEEE Reliability Test System (IEEE-RTS) is used to examine the proposed model and the impact of wind speed dependence between different wind regimes on the generation system reliability. The results confirm that the wind speed dependence has a negative effect on the generation system reliability.