• 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 Digital Convergence
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• v.11 no.10
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• pp.305-311
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• 2013

The size of the market as the economy continues to evolve, in order to make the right decisions to accurately predict the economic problems the market has emerged as an important issues. To express the modern economic system, the largest of the various economic indicators, pillars stock indicators analysis and decision-making with a proper understanding of the problem for the application of the model is suitable for time-series data concealment HMM. Based on this time series model and the calculation of the time and cost savings dimension decrease techniques for the estimation and prediction of the model was applied to the problem was to verify the validity. As a result, the model predictions in both the short term rather than long-term predictions of the model estimates the optimal predictive value similar pattern very similar to both the actual data and was able to confirm that.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.35-37
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• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.221-237
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• 2015

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.6
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• pp.758-769
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• 2004

The trends and seasonalities of most time series have a large variability. The result of the Singular Spectrum Analysis(SSA) processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, forecasting by the SSA method should be applied to time series governed (may be approximately) by linear recurrent formulae(LRF). This study examined forecasting ability of SSA-LRF model. These methods are applied to daily water demand data. These models indicate that most cases have good ability of forecasting to some extent by considering statistical and visual assessment, in particular forecasting validity shows good results during 15 days.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.3
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• pp.43-50
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• 2006

In this successive study, the analytical realization of coupled system was introduced using the times series identification and spectrum analysis, which was compared with conventional FFT spectrum. Also, the numerical responses of second order system, which is coupled, were solved using the numerical calculation of Runge-Kutta Gill method. After numerical analysis, the displacement, velocity and acceleration were acquired. Among them, the response of displacement was used for the analysis of time series spectrum. Among several time series, the ARMAX algorithm was proved to be appropriate for the spectrum analysis of the coupled system. Using the separated response of 1st and 2nd mode, the mode was calculated separately. And the responses of mixed modes were also analyzed for calculation of the mixed modes in the coupled system.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.643-648
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• 2006

It is important to understand the patterns of forest fire in terms of effective prevention and suppression activities. In this study, the monthly forest fire occurrences and their burned areas were investigated to enhance the understanding of the patterns of forest fire in Korea. The statistics of forest fires in Korea, 1970 through 2005, built by Korea Forest Service was analyzed by using time series analysis technique to fit ARIMA models proposed by Box-Jenkins. The monthly differences in forest fire characteristics were clearly distinguished, with 59% of total forest fire occurrences and 72% of total burned area being in March and April. ARIMA(1, 0, 1) was the best fitted model to both the fire accurrences and the burned area time series. The fire time series have a strong relation to the fire occurrences and the burned area of 1 month and 12 months before.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.155-160
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• 2015

We say that human have an animal of emotion. There are various kind in the emotion of human. One of among them, love has been studied in sociology and psychology as a matter of great concern. In this paper, we propose a novel love model with the delay time as response time for love. We also consider it in the Romeo and Juliet of love model to analyze their romantic behaviors. First we consider the Juliet only have a time delay, Romeo only have a time delay, and both Romeo and Juliet have a time delay. We represent their behaviors as time series and phase portrait, and we analyze their difference.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.299-311
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• 1994

In time series data, atypical observations are not rare. Several approaches have been proposed to detect a single outlier, but the effectiveness of those procedures is in doubt when patchy outliers are present. In this paper, the atypicality in patchy outliers is interpreted as a local structural change, and a model is introduced to entertain its effect on the series. Based on this model, a statistic and a procedure are proposed for identifying those local structural changes. The performance of the proposed procedure is evaluated through simulation study and the analysis of real data sets.