• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.145-156
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• 2023

Trajectory of ballistic missile is defined by inherent flight dynamics, which decided range and maneuvering characteristics. It is crucial to predict range and maneuvering characteristics of ballistic missile in KAMD (Korea Air and Missile Defense) to minimize damage due to ballistic missile attacks, Nowadays, needs for applying AI(Artificial Intelligence) technologies are increasing due to rapid developments of DNN(Deep Neural Networks) technologies. To apply these DNN technologies amount of data are required for superviesed learning, but trajectory data of ballistic missiles is limited because of security issues. Trajectory data could be considered as multivariate time series including many variables. And augmentation in time series data is a developing area of research. In this paper, we tried to augment trajectory data of ballistic missiles using recently developed methods. We used TimeVAE(Time Variational AutoEncoder) method and TimeGAN(Time Generative Adversarial Networks) to synthesize missile trajectory data. We also compare the results of two methods and analyse for future works.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.747-757
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• 2017

We investigate multivariate volatilities based on high frequency time series. The PCA (principal component analysis) method is employed to achieve a dimension reduction in multivariate volatility. Multivariate realized volatilities (RV) with various frequencies are calculated from high frequency data and "optimum" frequency is suggested using PCA. Specifically, RVs with various frequencies are compared with existing daily volatilities such as Cholesky, EWMA and BEKK after dimension reduction via PCA. An analysis of high frequency stock prices of KOSPI, Samsung Electronics and Hyundai motor company is illustrated.

• Journal of Environmental Science International
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• v.16 no.3
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• pp.369-376
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• 2007

This study was conducted to evaluate water quality in coastal sea of Kunsan using multivariate analysis. The analysis data in Coastal Sea of Kunsan use of surveyed data by the NFRDI from April 2000 to November 2002. Twelve water Quality parameter were determined on each sample. The results was summarized as follow ; Water quality in coastal sea of Kunsan could be explained up to 62.782% by four factors which were included in loading of nitrogen-nutrients by Keum river(24.688%), suspended solids variation (12.180%), seasonal climate variation (18.367%) and variation of DIP (10.546%). To analyze spatially and monthly variation by factor score, it was divided by inner area and outer area spatially, and spring and summer monthly. The result of time series analysis by factor score, inner area of Kunsan coastal sea(St.1 and St. 2) was the most affected by nitrogen-nutrient and suspended solids due to runoff by Keum river. It could be suggested from these results that it is important to reduce tile pollution loads from Kuem river for the control of the water quality in coastal sea of Kunsan.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.473-481
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• 2010

Forecasting for air demand such as international passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison of the performances of the multivariate time series models. In this paper, we used real data such as exchange rates, oil prices and export amounts to predict the future demand on international passenger and freight.

• Asian Journal of Business Environment
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• v.11 no.4
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• pp.17-29
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• 2021

Purpose: The aim of this study is to explore the dynamic linkage between conditional stock market volatility and macroeconomic uncertainty of Bangladesh. Research design, data, and methodology: This study uses monthly data covering the time period from January 2005 to December 2018. A comprehensive set of macroeconomic variables, namely industrial production index (IP), consumer price index (CPI), broad money supply (M2), 91-day treasury bill rate (TB), treasury bond yield (GB), exchange rate (EX), inflow of foreign remittance (RT) and stock market index of DSEX are used for analysis. Symmetric and asymmetric univariate GARCH family of models and multivariate VAR model, along with block exogeneity and impulse response functions, are implemented on conditional volatility series to discover the possible interactions and causal relations between macroeconomic forces and stock return. Results: The analysis of the study exhibits time-varying volatility and volatility persistence in all the variables of interest. Moreover, the asymmetric effect is found significant in the stock return and most of the growth series of macroeconomic fundamentals. Results from the multivariate VAR model indicate that only short-term interest rate significantly influence the stock market volatility, while conditional stock return volatility is significant in explaining the volatility of industrial production, inflation, and treasury bill rate. Conclusion: The findings suggest an increasing interdependence between the money market and equity market as well as the macroeconomic fundamentals of Bangladesh.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.261-270
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• 2009

Current financial crisis triggered by shaky U.S. banking system adds to the emphasis on the importance of the volatility in controlling and understanding financial time series data. The ARCH and GARCH models have been useful in analyzing economic time series volatilities. In particular, multivariate GARCH(MGARCH, for short) provides both volatilities and conditional correlations between several time series and these are in turn applied to computations of hedge-ratio and VaR. In this short article, we try to assess various MGARCH models with respect to the back-testing performances in VaR study. To this end, 14 korean stock prices are analyzed and it is found that MGARCH outperforms rolling window, and BEKK and CCC are relatively conservative in back-testing performance.

• Journal of Environmental Science International
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• v.15 no.11
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• pp.1017-1025
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• 2006

This study was conducted to evaluate characteristic of water duality in coastal sea of Incheon using a multivariate analysis. The analysis data in coastal sea of Incheon was aquired by the NFRDI data which was surveyed from March 1997 to November 2003. Eleven water quality parameters were determined on each survey The results were summarized as follow : Water quality in Incheon coastal sea could be explained up to 64.62% by three factors which were included in loading of fresh water and nutrients by the land(36.98%), seasonal variation(16.19%), and internal metabolism (11.24%). The results of time series analysis by factor score, in case of factor 1, station 1 influenced by Han river was shown to high factor score and station 3 located by outer sea was shown to low factor score. In case of factor 2, station 1 was appeared to high variation and station 3 was appeared to low variation. The result of cluster analysis by station was classified into three group that has different water quality characteristics. Especially, station 1 which affected by Han river and station 4 which affected by sewage treatment plant was appeared to considerable water quality characteristics against other station. In yearly cluster analysis, three group was classified and water quality in 2003 years due to high precipitation was different to another year. It could be suggested from these results that it is important to control discharge of fresh water by Han rivet and sewage treatment plant for water quality management of coastal sea of Incheon.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.13-20
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• 2023

In this paper, we propose a water temperature prediction method using feature extraction and reconstructed data based on LSTM-Autoencoder. We used multivariate time series data such as sea surface water temperature in the Naksan area of the East Sea where the cold water zone phenomenon occurred, and wind direction and wind speed that affect water temperature. Using the LSTM-Autoencoder model, we used three types of data: feature data extracted through dimensionality reduction of the original data combined with multivariate data of the original data, reconstructed data, and original data. The three types of data were trained by the LSTM model to predict sea surface water temperature and evaluated the accuracy. As a result, the sea surface water temperature prediction accuracy using feature extraction of LSTM-Autoencoder confirmed the best performance with MAE 0.3652, RMSE 0.5604, MAPE 3.309%. The result of this study are expected to be able to prevent damage from natural disasters by improving the prediction accuracy of sea surface temperature changes rapidly such as the cold water zone.