• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.149-160
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• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• Asian Journal of Business Environment
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• v.12 no.3
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• pp.1-12
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• 2022

Purpose: This study aims to investigate whether AIS data can be used as a supporting indicator or as an initial signal to describe Indonesia's export-import conditions in real-time. Research design, data, and methodology: This study performs several stages of data selection to obtain indicators from AIS that truly reflect export-import activities in Indonesia. Also, investigate the potential of AIS indicators in producing forecasts of the value and volume of Indonesian export-import using conventional statistical methods and machine learning techniques. Results: The six preprocessing stages defined in this study filtered AIS data from 661.8 million messages to 73.5 million messages. Seven predictors were formed from the selected AIS data. The AIS indicator can be used to provide an initial signal about Indonesia's import-export activities. Each export or import activity has its own predictor. Conventional statistical methods and machine learning techniques have the same ability both in forecasting Indonesia's exports and imports. Conclusions: Big data AIS can be used as a supporting indicator as a signal of the condition of export-import values in Indonesia. The right method of building indicators can make the data valuable for the performance of the forecasting model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.44-45
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• 2020

Korea has the fourth highest CO₂ emission among OECD countries in 2018, As of 2019, total greenhouse gas emissions per capita increased by about 98.2% in comparison to 1990. Korea has promised a 37% reduction in greenhouse gas emissions in 2030 from the projected Paris Climate Change Accord. Currently, many countries use the emissions trading system(ETS) for international carbon management. In 2015, ETS has been implemented in Korea, and the importance of calculating CO₂ emissions from construction machinery has increased. So, we require an accurate calculation of the environmental charges through the allocated CERs. Using the CER price and related search keywords, this paper derive about prediction models of CER price and compare and focus on more accurate prediction about CER price. By this method, the budget needed to establish the initial construction process plan can be calculated based on more accurate predicted CER price.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.5-14
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• 2017

Korea, which has undergone a rapid urbanization, faces various problems such as the management of facilities, safety, environment and transportation. To solve civil complaints, local governments receive electronic complaints, but complaints are increasing. Therefore, this study conducted the spatial distribution pattern analysis and the trend analysis by presenting location data on spatial information through Geo-coding by collecting electronic civil petition data over the last 10 years targeting Jinju city. Using the ARIMA model, this study predicted the occurrence of complaints over the next two years (2016~2017) through a time series forecast analysis. As a result, the complaints related to illegal parking were the highest, the complaint related to noise was the second highest, and the complaints related to illegal garbage dumping was the third highest. In addition, the analysis of the spatial distribution pattern shows that the largest hot spot was formed in the central commercial district every year. As a result of the time series forecasting analysis for the crackdown of the illegal parking, complaints increased slightly. To compare the predicted value and the actual data showed a similar pattern. It is judged that this study will be utilized to establish effective countermeasures against civil complaints.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.579-599
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• 2014

The purpose of this study was to identify the trends and risk of bias of research using time series analysis on health and welfare in Korea and to suggest a direction for future health and welfare research. The database searches identified 6,543 papers. Following the process for screening and selecting, a total of 91 papers were included in the systematic review. There has been a steady increase in the number of articles using time series analysis from 1987 to 2013. Time series analysis was applied in medicine and health science journals. The main goals were explanation and description. Most of the subjects were heath status and utilization of healthcare services. The main model used in the time series analysis was ARIMA followed by time series regression. The data were gathered from various sources, including the national statistical office and government agencies. For assessing risk of bias, some studies were found to have inadequate sample sizes or showed no time series graphs and plots. These findings suggest greater widespread utilization of time series analysis in the field of health and welfare and to use the appropriate analysis methods and statistical procedures to obtain more reliable results to improve the quality of research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.228-234
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• 2021

Real estate market participants need to have a sense of predicting real estate prices in decision-making. Commonly used methodologies, such as regression analysis, ARIMA, and VAR, have limitations in predicting the value of an asset, which fluctuates due to unknown variables. Therefore, to mitigate the limitations, an artificial neural was is used to predict the price trend of apartments in Seoul, the hottest real estate market in South Korea. For artificial neural network learning, the learning model is designed with 12 variables, which are divided into macro and micro factors. The study was conducted in three ways: (Ed note: What is the difference between case 1 and 2? Is case 1 micro factors?)CASE1 with macro factors, CASE2 with macro factors, and CASE3 with the combination of both factors. As a result, CASE1 and CASE2 show 87.5% predictive accuracy during the two-year experiment, and CASE3 shows 95.8%. This study defines various factors affecting apartment prices in macro and microscopic terms. The study also proposes an artificial network technique in predicting the price trend of apartments and analyzes its effectiveness. Therefore, it is expected that the recently developed learning technique can be applied to the real estate industry, enabling more efficient decision-making by market participants.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.365-373
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• 2016

Much of the data used in the analysis of environmental ecological data is being obtained over time. If the number of time points is small, the data will not be given enough information, so repeated measurements or multiple survey points data should be used to perform a comprehensive analysis. The method used for that case is longitudinal data analysis or mixed model analysis. However, if the amount of information is sufficient due to the large number of time points, repetitive data are not needed and these data are analyzed using time series analysis technique. In particular, with a large number of data points in the current situation, when we want to predict how each variable affects each other, or what trends will be expected in the future, we should analyze the data using time series analysis techniques. In this study, we introduce univariate time series analysis, intervention time series model, transfer function model, and multivariate time series model and review research papers studied in Korea. We also introduce an error correction model, which can be used to analyze environmental ecological data.

• Journal of KIISE
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• v.44 no.9
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• pp.954-965
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• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• The Journal of Fisheries Business Administration
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• v.38 no.2
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• pp.41-62
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• 2007

The Fish aquacultural Industry has been developed rapidly since 1990s in Korea. The total production of fish aquaculture was 5,000ton in the beginning of 1990s, but it was an excess of 80,000ton in 2005. In the beginning of 1990s, the percentage of flatfish yield was 80% of the fish aquaculture in the respect of production. And it has been maintained 50% level in 2005. In this point of view, flatfish aquaculture played the role of leader in the development of fish aquaculture. Rapid increasing of production was not only caused to decreasing in price basically, but also it threatened the management of producer into insecure price for aquacultural flatfish. Therefore, it needs the policy for stabilizing in price, but it is difficult to choose the method because the basic study was not accomplished plentifully. This study analyzed about price structure of aquacultural flatfish. A period of analysis was from January 2000 to December 2005, and a data was used monthly data for price. The principal result of this study is substantially as follows. 1) The price of producing and consuming district is closely connected. 2) A gap between producing district price and consuming district price is decreasing recently, It seems to be correlated with outlook business of aquacultural flatfish. 3) Trend line of the price was declining until 2002, but it turned up after that. The other side, circulated fluctuation was being showed typically. 4) The circle of circulated fluctuation was growing longer, so it seems that the producer was doing a sensible productive activity to cope with changing price. As a result, government's policy needs to be turned into price policy from policy of increased production for aquacultural flatfish. It seems that the best policy is price stabilization polices. And also, government needs to invest in outlook business for aquaculture constantly.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.