• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.807-819
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• 2013

A recurrence plot can be used as a graphical exploratory data analysis tool before confirmatory time series analysis. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in a time series at a glance. Korean stock data shows the usefulness of the recurrence plot as a graphical exploratory data analysis tool for time series data.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.161-167
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• 2015

It is very difficult to predict time-series data. This is because data obtained from the signal having a non-linear characteristic has an uncertainty. In this paper, By differentiating time-series data is the average of the past data under the premise that change depending on what pattern, and find the soft look of time-series change pattern. This paper also apply the probability variables to generalize time-series data having a specific data according to the reflection ratio of the differentiation. The predicted value is estimated by removing cyclic movement and seasonal fluctuation, and reflect the trend by extracting the irregular fluctuation. Predicted value has demonstrated the superiority of the proposed algorithm and compared with the best results by a simple moving average and the moving average.

• Journal of Korea Water Resources Association
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• v.42 no.4
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• pp.355-363
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• 2009

The purpose of this paper is to investigate the influence of noise on chaotic time series. We used two time series of Lorenz system and of Great Salt Lake's volume data which are well known as chaotic systems. This study investigated the attractors, correlation dimensions, and Close Returns Plots and Close Returns Histograms of two time series to investigate the influence of noise as increasing noise level. We performed Chi-square test to the relative frequency of Close Returns Histogram from Close Returns Plot for the investigation of stochastic process of chaotic time series as increasing noise level of time series. As the results, two time series were changed from chaotic to stochastic series as noise level is increased. Finally, we analyzed the effect of noise cancellation by using Simple Moving Average method. The results of applications of Simple Moving Average method to Lorenz and GSL time series showed that we could effectively cancel the noise. Then we could confirm the applicability of Simple Moving Average method to cancel the noise for the hydrologic time series having chaotic characteristics.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.1-11
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• 2023

In this paper, instead of using recurrent neural network, we compare a classification performance of time series imaging algorithms using convolution neural network. There are traditional algorithms that imaging time series data (e.g. GAF(Gramian Angular Field), MTF(Markov Transition Field), RP(Recurrence Plot)) in TSC(Time Series Classification) community. Furthermore, we compare STFT(Short Time Fourier Transform) algorithm that can acquire spectrogram that visualize feature of voice data. We experiment CNN's performance by adjusting hyper parameters of imaging algorithms. When evaluate with GunPoint dataset in UCR archive, STFT(Short-Time Fourier transform) has higher accuracy than other algorithms. GAF has 98~99% accuracy either, but there is a disadvantage that size of image is massive.

• Smart Structures and Systems
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• v.11 no.3
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• pp.299-314
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• 2013

This paper presents a nonlinear time series analysis technique for evaluating machine defect severity, based on the Recurrence Plot (RP) entropy. The RP entropy is calculated from the probability distribution of the diagonal line length in the recurrence plot, which graphically depicts a system's dynamics and provides a global picture of the autocorrelation in a time series over all available time-scales. Results of experimental studies conducted on a spindle-bearing test bed have demonstrated that, as the working condition of the bearing deteriorates due to the initiation and/or progression of structural damages, the frequency information contained in the vibration signal becomes increasingly complex, leading to the increase of the RP entropy. As a result, RP entropy can serve as an effective indicator for defect severity assessment of rolling bearings.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1103-1112
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• 2013

Exploratory data analysis focuses mostly on data exploration instead of model fitting. We can use the recurrence plot as a graphical exploratory data analysis tool. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in time series at a glance.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• Speech Sciences
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• v.3
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• pp.148-155
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• 1998

As an application of dimensional analysis in the theory of chaos and fractals, we studied and estimated the information dimension for various phonemes. By constructing phase-space vectors from the time-series speech signals, we calculated the natural measure and the Shannon's information from the trajectories. The information dimension was finally obtained as the slope of the plot of the information versus space division order. The information dimension showed that it is so sensitive to the waveform and time delay. By averaging over frames for various phonemes, we found the information dimension ranges from 1.2 to 1.4.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• Journal of Preventive Medicine and Public Health
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• v.46 no.1
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• pp.10-18
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• 2013

Objectives: We investigated the association between particulate matter less than $10{\mu}m$ in aerodynamic diameter ($PM_{10}$) exposure and non-accidental mortality in Asian populations by meta-analysis, using both time-series and case-crossover analysis. Methods: Among the 819 published studies searched from PubMed and EMBASE using key words related to $PM_{10}$ exposure and non-accidental mortality in Asian countries, 8 time-series and 4 case-crossover studies were selected for meta-analysis after exclusion by selection criteria. We obtained the relative risk (RR) and 95% confidence intervals (CI) of non-accidental mortality per $10{\mu}g/m^3$ increase of daily $PM_{10}$ from each study. We used Q statistics to test the heterogeneity of the results among the different studies and evaluated for publication bias using Begg funnel plot and Egger test. Results: Testing for heterogeneity showed significance (p<0.001); thus, we applied a random-effects model. RR (95% CI) per $10{\mu}g/m^3$ increase of daily $PM_{10}$ for both the time-series and case-crossover studies combined, time-series studies relative risk only, and case-crossover studies only, were 1.0047 (1.0033 to 1.0062), 1.0057 (1.0029 to 1.0086), and 1.0027 (1.0010 to 1.0043), respectively. The non-significant Egger test suggested that this analysis was not likely to have a publication bias. Conclusions: We found a significant positive association between $PM_{10}$ exposure and non-accidental mortality among Asian populations. Continued investigations are encouraged to contribute to the health impact assessment and public health management of air pollution in Asian countries.