• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.107-134
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• 2007

This paper tests the relationship among returns, volatilities, contracts and open interests of KOSPI 200 futures markets with the various dynamic models such as granger-causality, impulse response, variance decomposition and ARMA(1, 1)-GJR-GARCH(1, 1)-M. The sample period is from July 7, 1998 to December 29, 2005. The main empirical results are as follows; First, both contract change and open interest change of KOSPI 200 futures market tend to lead the returns of that according to the results of granger-causality, impulse response and variance decomposition with VAR. These results are likely to support the KOSPI 200 futures market seems to be inefficient with rejecting the hypothesis 1. Second, we also find that the returns and volatilities of the KOSPI 200 futures market are effected by both contract change and open interest change of that due to the results of ARMA(1,1)-GJR-GARCH(1,1)-M. These results also reject the hypothesis 1 and 2 suggesting the evidences of inefficiency of the KOSPI 200 futures market. Third, the study shows the asymmetric information effects among the variables. In addition, we can find the feedback relationship between the contract change and open interest change of KOSPI 200 futures market.

• Current Research on Agriculture and Life Sciences
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• v.8
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• pp.129-138
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• 1990

This study investigated the relationship between the cutflow of rural population and agricultural policy by using time series method. For the analytical tools, decomposition time series methods and regression technique were employed in computing seasonal fluctuation and cyclical fluctuation of population migration. Also, this study predicted farmhouse, rural population till the 2000's by means of the mathematical methods. The analytical forms employed in forecasting farmhouse, rural population were Exponential curve, Gompertz curve and Transcendental form. The major findings of this study were identified as follows: 1) Rural population and farmhouse population began to decrease from 1965 and hastily went down since 1975. Rural population which accounted for 36.4 percent, 35.6 percent of national population respectively in 1960 diminished about two times: 17.5 percent, 17.1 percent respectively. 2) The rapid decreasing of the rural population was caused because of the outflow of rural people to the urban regions. Of course, that was also caused from the natural decreases but the main reason was heavily affected more the former than the latter. In the outflowing course shaped from rural to the urban regions, rural people concentrated on such metropolis as Seoul, Pusan, Keanggi. But these trends were diminishing slowly. On the other hand, compared with that of the 1970's the migration to Keanggi was still increasing in the 1980's. That is, people altered the way of migration from the migration to Seoul, Pusan to the migration to the out-skirts of Seoul. 3) The seasonal fluctuation index of population migration has gone down since the June which the request of agricultural labor force increases and has turned to be greatly wanted in the March as result of decomposition time series method. As result of cyclical analysis, the cyclical patterns of migration have greatly 7 cycle. 4) As result of forecasting the rural and farmhouse population, rural and farmhouse population in the 2000 will be about 9,655(thousand/people) and 4,429(thousand/people) respectively. Thus, it is important to analyze the probloms that rural and farmhouse population will decrease or increase by the degree. But fairly defining the agricultural into a industry that supply the food, this problem - how much our nation need the rural and farmhouse population - is greatly significant too. Therefore, the basic problems of the agricultural including the outflows of rural people are the earning differentials between rural and urban regions. And we should regard the problems of the gap of relative incomes between rural and urban regions as the main task of the agricultural policy and treat the agricultural policy in the viewpoint of developing economic equilibrium than efficiency by using actively the natural resources of the rural regions.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.243-266
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• 2006

This paper tests cross hedging performance of the KOSPI 200 stock index futures to hedge the downside risk of the KOSPI, KOSPI 200 and KOSDAQ50 spot market. For this purpose we introduce the minimum variance hedge model, bivariate GARCH(1,1) and EGARCH(1,1) model as hedge models. The main results are as follows; First, we find that the direct hedge performance of KOSPI 200 index futures is better than those of indirect hedge performance. second, in case or cross hedge performance the hedge effect of KOSPI 200 stock index futures market against KOSPI 200 stock index spot market is relatively better than those of KOSPI 200 index futures against KOSPI and KOSDAQ spot position. Third, for the out-sample, hedging effectiveness of the risk-minimization with constant hedge ratios is higher than those of the time varying bivariate GARCH(1,1) and EGARCH(1,1) model. In conclusion, investors are encouraged to use simple risk-minimization model rather than the time varying hedge models like GARCH and EGARCH model to hedge the position of the Korean stock index cash markets.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.133-144
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• 2016

In this study, Frequency Domain Reflectometry (FDR) and COSMIC-ray soil moisture (SM) stations were installed at Sungkyunkwan University in Suwon, South Korea. To provide reliable information about SM, soil property test, time series analysis of measured soil moisture, and comparison of measured SM with satellite-based SM product are conducted. In 2014, six FDR stations were set up for obtaining SM. Each of the stations had four FDR sensors with soil depth from 5 cm to 40 cm at 5~10 cm different intervals. The result showed that study region had heterogeneous soil layer properties such as sand and loamy sand. The measured SM data showed strong coupling with precipitation. Furthermore, they had a high correlation coefficient and a low root mean square deviation (RMSD) as compared to the satellite-based SM products. After verifying the accuracy of the data in 2014, four FDR stations and one COSMIC-ray station were additionally installed to establish the Soil Moisture site with FDR and COSMIC-ray, called SM-FC. COSMIC-ray-based SM had a high correlation coefficient of 0.95 compared with mean SM of FDR stations. From these results, the SM-FC will give a valuable insight for researchers into investigate satellite- and model-based SM validation study in South Korea.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.665-679
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• 2019

Mesoscale soil moisture measurement from the promising Cosmic-Ray Neutron Probe (CRNP) is expected to bridge the gap between large scale microwave remote sensing and point-based in-situ soil moisture observations. Traditional calibration based on $N_0$ method is used to convert neutron intensity measured at the CRNP to field scale soil moisture. However, the static calibration parameter $N_0$ used in traditional technique is insufficient to quantify long term soil moisture variation and easily influenced by different time-variant factors, contributing to the high uncertainties in CRNP soil moisture product. Consequently, in this study, we proposed a modified traditional calibration method, so-called Dynamic-$N_0$ method, which take into account the temporal variation of $N_0$ to improve the CRNP based soil moisture estimation. In particular, a nonlinear regression method has been developed to directly estimate the time series of $N_0$ data from the corrected neutron intensity. The $N_0$ time series were then reapplied to generate the soil moisture. We evaluated the performance of Dynamic-$N_0$ method for soil moisture estimation compared with the traditional one by using a weighted in-situ soil moisture product. The results indicated that Dynamic-$N_0$ method outperformed the traditional calibration technique, where correlation coefficient increased from 0.70 to 0.72 and RMSE and bias reduced from 0.036 to 0.026 and -0.006 to $-0.001m^3m^{-3}$. Superior performance of the Dynamic-$N_0$ calibration method revealed that the temporal variability of $N_0$ was caused by hydrogen pools surrounding the CRNP. Although several uncertainty sources contributed to the variation of $N_0$ were not fully identified, this proposed calibration method gave a new insight to improve field scale soil moisture estimation from the CRNP.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.28 no.1
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• pp.1-18
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• 2023

The East Sea, one of the regions where the most rapid warming is occurring, is known to have important implications for the response of the ocean to future climate changes because it not only reacts sensitively to climate change but also has a much shorter turnover time (hundreds of years) than the ocean (thousands of years). However, the processes underlying changes in seawater characteristics at the sea's deep and abyssal layers, and meridional overturning circulation have recently been examined only after international cooperative observation programs for the entire sea allowed in-situ data in a necessary resolution and accuracy along with recent improvement in numerical modeling. In this review, previous studies on the physical characteristics of seawater at deeper parts of the East Sea, and meridional overturning circulation are summarized to identify any remaining issues. The seawater below a depth of several hundreds of meters in the East Sea has been identified as the Japan Sea Proper Water (East Sea Proper Water) due to its homogeneous physical properties of a water temperature below 1℃ and practical salinity values ranging from 34.0 to 34.1. However, vertically high-resolution salinity and dissolved oxygen observations since the 1990s enabled us to separate the water into at least three different water masses (central water, CW; deep water, DW; bottom water, BW). Recent studies have shown that the physical characteristics and boundaries between the three water masses are not constant over time, but have significantly varied over the last few decades in association with time-varying water formation processes, such as convection processes (deep slope convection and open-ocean deep convection) that are linked to the re-circulation of the Tsushima Warm Current, ocean-atmosphere heat and freshwater exchanges, and sea-ice formation in the northern part of the East Sea. The CW, DW, and BW were found to be transported horizontally from the Japan Basin to the Ulleung Basin, from the Ulleung Basin to the Yamato Basin, and from the Yamato Basin to the Japan Basin, respectively, rotating counterclockwise with a shallow depth on the right of its path (consistent with the bottom topographic control of fluid in a rotating Earth). This horizontal deep circulation is a part of the sea's meridional overturning circulation that has undergone changes in the path and intensity. Yet, the linkages between upper and deeper circulation and between the horizontal and meridional overturning circulation are not well understood. Through this review, the remaining issues to be addressed in the future were identified. These issues included a connection between the changing properties of CW, DW, and BW, and their horizontal and overturning circulations; the linkage of deep and abyssal circulations to the upper circulation, including upper water transport from and into the Western Pacific Ocean; and processes underlying the temporal variability in the path and intensity of CW, DW, and BW.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.