• 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.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.113-120
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• 2015

Technology analysis is to analyze technological data such as patent and paper for a given technology field. From the results of technology analysis, we can get novel knowledge for R&D planing and management. For the technology analysis, we can use diverse methods of statistics. Time series analysis is one of efficient approaches for technology analysis, because most technologies have researched and developed depended on time. So many technological data are time series. Time series data are occurred through time. In this paper, we propose a methodology of technology forecasting using the dynamic time warping (DTW) of time series analysis. To illustrate how to apply our methodology to real problem, we perform a case study of patent documents in target technology field. This research will contribute to R&D planning and technology management.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.675-683
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• 2015

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

• Journal of Preventive Medicine and Public Health
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• v.22 no.2 s.26
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• pp.189-196
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• 1989

In January 1988, district medical insurance plan was executed on a national scale in Korea. We conducted an evaluation of the impact of execution of district medical insurance plan on number of hospital patients: number of outpatients; and occupancy rate. This study was carried out by Box-Jenkins time series analysis. We tested the statistical significance with intervention component added to ARIMA model. Results of our time series analysis showed that district medical insurance plan had a significant effect on the number of outpatients and occupancy rate. Due to this plan the number of outpatients had increased by 925 patients every month which is equivalent to 8.3 percents of average monthly insurance outpatients in 1987, and occupancy rate had also increased by 0.12 which is equivalent to 16 percents of that in 1987.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.415-434
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• 2015

In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.

• Water for future
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• v.14 no.2
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• pp.71-79
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• 1981

This study is established of simulation models form the stochastic and statistic analysis of monthly rainfall and monthly runoff on south Han river. The time series simulation of monthly runoff is introduced with a linear stochastic model for simulating synthetic monthly runoff data. And, time series model of monthly pricipitation and monthly runoff is introduced to be a pure random time series with known statical parameter, which is characterized by an exponential recession curve with one parameter, and is develope expressing the statistical parameter for length of carryover.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.357-362
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• 2012

We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.