• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.329-338
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• 2017

This study attempts to classify 1008 alley markets in Seoul through cluster analysis using Dynamic Time Warping, one of the methods used to analyze the similarity of time series, and evaluate the possibility of opening new stores. The sequence of the gross sales of an alley market and that of gross sales per store stand for the potential of growth and profitability of the market, respectively and are used as variables for cluster analysis. Five clusters are obtained for the gross sales and four clusters for the gross sales per store. These two types of clusters are again classified as rising and falling trends, respectively, and the combination of these trends produces four categories. These categories are used to evaluate the possibility of opening new stores in alley markets. The results show that the southeast which is relatively wealthy inferior to other regions in opening new stores. Alley markets in the northeast and the southwest are better than other regions such that opening a new store is justified. In the northwest, there are many markets with trend of gross sales and that of gross sales per store moving in opposite directions, and new store openings in these markets should be postponed.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.44-51
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• 2008

Pattern matching and pattern searching in time series data have been active issues in a number of disciplines. This paper suggests a novel pattern matching technology which can be used in the field of stock market analysis as well as in forecasting stock market trend. First, we define conceptual patterns, and extract data forming each pattern from given time series, and then generate learning model using Hidden Markov Model. The results show that the context-based pattern matching makes the matching more accountable and the method would be effectively used in real world applications. This is because the pattern for new data sequence carries not only the matching itself but also a given context in which the data implies.

• The Journal of Information Systems
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• v.30 no.3
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• pp.201-216
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• 2021

Purpose The purpose of this study is to analyze the trend of patent technology in textile materials using text mining methodology based on Dynamic Embedded Topic Model and Structural Topic Model. It is expected that this study will have positive impact on revitalizing and developing textile materials industry as finding out technology trends. Design/methodology/approach The data used in this study is 866 domestic patent text data in textile material from 1974 to 2020. In order to analyze technology trends from various aspect, Dynamic Embedded Topic Model and Structural Topic Model mechanism were used. The word embedding technique used in DETM is the GloVe technique. For Stable learning of topic modeling, amortized variational inference was performed based on the Recurrent Neural Network. Findings As a result of this analysis, it was found that 'manufacture' topics had the largest share among the six topics. Keyword trend analysis found the fact that natural and nanotechnology have recently been attracting attention. The metadata analysis results showed that manufacture technologies could have a high probability of patent registration in entire time series, but the analysis results in recent years showed that the trend of elasticity and safety technology is increasing.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.165-172
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• 2017

In this paper, we analyze the research / technology trends of electric vehicles from 2001 to 2014, through keyword analysis using paper data published in SCIE or SSCI Journal on electric vehicles, time series analysis using patent data by IPC, and network analysis using nodeXL. also we predicted promising technologies of electric vehicles using one of the prediction methods, weighted moving average method. As a result of this study, battery technology among the electric vehicle component technologies appeared as a promising technology.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.1-13
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• 2023

This study forecasted the manpower demand of eco-friendly smart shipbuilding, whose importance and weight are increasing according to the environmental regulations of the IMO and the spread of the 4th industrial revolution technology. It predicted the shipbuilding industry manpower by applying various models of trend analysis and time series analysis based on data from 2000 to 2020 of Statistics Korea. It was found that the prediction applying geometric mean had the smallest gap among the trend and time series analysis methods in comparing between forecast results and actual data for the past 5 years. Therefore, the demand for manpower in the shipbuilding industry was predicted by using the geometric mean method. In addition, the manpower demand of smart eco-friendly ships wast forecasted by using the 2018 and 2020 manpower survey results of the Ministry of Trade, Industry and Energy and reflecting the trend of manpower increase in the shipbuilding industry. The result of forecasting showed that 62,001 person in 2025 and 85,035 people in 2030. This study is expected to contribute to the adjustment of manpower supply and demand and the training professional manpower in the future by increasing the accuracy of forecasting for high value-added eco-friendly smart ships.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.17-22
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• 2024

History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.

• Journal of Soil and Groundwater Environment
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• v.20 no.7
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• pp.112-120
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• 2015

Statistical trend analysis using the data from the National Groundwater Quality Monitoring Network (NGQMN) of Korea was conducted to establish a new groundwater quality management framework. Sen’s test, a non-parametric statistical method for trend analysis, was used to determine the linear trend of the groundwater quality data. The analysis was conducted at different confidence levels (i.e., at 70, 80, 90, 95, and 99% confidence levels) for three of groundwater quality parameters, i.e., nitrate-nitrogen, chloride, and pH, which have sufficient time series of the NGQMN data between 2007 and 2013. The results showed that different trends can be determined for different depths even for the same monitoring site and the numbers of wells having significant trends vary with different confidence levels. The wells with increasing or decreasing trends were far less than the wells with no trend. Chloride had more wells with increasing trend than other parameters. On the other hand, nitrate-nitrogen had the most wells with increasing trend and concentration exceeding 75% of the threshold values (TVs). Based on the methodology used for this study, we suggest including groundwater TVs and trend analysis to evaluate groundwater quality and to establish an advanced groundwater quality management framework.

• Journal of Fashion Business
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• v.15 no.6
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• pp.176-203
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• 2011

In today's intensifying global competition, Korean fashion industry is relying on only qualitative data for feasibility study of future projects and developmental plan. This study was conducted in order to support establishment of a scientific and rational management system that reflects market demand. First, fashion market size was limited to the total amount of expenditure for fashion clothing products directly purchased by Koreans for wear during 6 months in spring and summer and 6 months in autumn and winter. Fashion market forecasting model was developed using statistical forecasting method proposed by previous research. Specifically, time series model was selected, which is a verified statistical forecasting method that can predict future demand when data from the past is available. The time series for empirical analysis was fashion market sizes for 8 segmented markets at 22 time points, obtained twice each year by the author from 1998 to 2008. Targets of the demand forecasting model were 21 research models: total of 7 markets (excluding outerwear market which is sensitive to seasonal index), including 6 segmented markets (men's formal wear, women's formal wear, casual wear, sportswear, underwear, and children's wear) and the total market, and these markets were divided in time into the first half, the second half, and the whole year. To develop demand forecasting model, time series of the 21 research targets were used to develop univariate time series models using 9 types of exponential smoothing methods. The forecasting models predicted the demands in most fashion markets to grow, but demand for women's formal wear market was forecasted to decrease. Decrease in demand for women's formal wear market has been pronounced since 2002 when casualization of fashion market intensified, and this trend was analyzed to continue affecting the demand in the future.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.933-951
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• 2009

Climate change of global warming may affect the water circulation in Korea. Rainfall is occurred with complex of multiple climatic indices. Therefore, the rainfall is one of the most significant index due to climate change in the process of water circulation. In this research, multiple time series data of rainfall events were extracted to represent the rainfall characteristics. In addition, the occurrence of rainfall time series analyzed by annual, seasonal and monthly data. Analysis method used change analysis of mean and standard deviation and trend analysis. Also, changes in rainfall characteristics and the relative error was calculated during the last 10 years for comparison with past data. At the results, significant statistical results weren't showed by randomness of rainfall data. However, amount of rainfall generally increased last 10 years, and number of raining days had trend of decrease. In addition, seasonal and monthly changes in the rainfall characteristics can be found to appear differently.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.3
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• pp.75-82
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• 2022

The purpose of the Consumer sentiment index survey is to determine the consumer's economic situation and consumption spending plan, and it is used as basic data for diagnosing economic phenomena and forecasting the future economic direction. The purpose of this paper is to analyze and predict the future Consumer sentiment index using the ARMA model based on the past consumer index. Consumer sentiment index is determined according to consumer trends, so it can reflect consumer realities. The consumer sentiment index is greatly influenced by economic indicators such as the base interest rate and consumer price index, as well as various external economic factors. If the consumer sentiment index, which fluctuates greatly due to consumer economic conditions, can be predicted, it will be useful information for households, businesses, and policy authorities. This study predicted the Consumer sentiment index for the next 3 years (36 months in total) by using time series analysis using the ARMA model. As a result of the analysis, it shows a characteristic of repeating an increase or a decrease every month according to the consumer trend. This study provides empirical results of prediction of Consumer sentiment index through statistical techniques, and has a contribution to raising the need for policy authorities to prepare flexible operating policies in line with economic trends.