• The Journal of the Korea Contents Association
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• v.6 no.6
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• pp.26-32
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• 2006

Predictions on stock prices have been a hot issue in stock market as people get more interested in stock investments. Assuming that the stock price is moving by a trend in a specific pattern, we believe that a model can be derived from past data to describe the change of the price. The best model can help predict the future stock price. In this paper, our model derivation is based on automata over temporal data to which the model is explicable. We use Bayesian Information Criterion(BIC) to determine the best number of states of the model. We confirm the validity of Bayesian Information Criterion and apply it to building models over stock price indices. The model derived for predicting daily stock price are compared with real price. The comparisons show the predictions have been found to be successful over the data sets we chose.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.22-30
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• 2007

Most real word systems such as world economy, stock market, and medical applications, contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of the system. In this paper, we investigated methods for best clustering over time series data. As a first step for clustering, BIC (Bayesian Information Criterion) approximation is used to determine the number of clusters. A search technique to improve clustering efficiency is also suggested by analyzing the relationship between data size and BIC values. For clustering, two methods, model-based and similarity based methods, are analyzed and compared. A number of experiments have been performed to check its validity using real data(stock price). BIC approximation measure has been confirmed that it suggests best number of clusters through experiments provided that the number of data is relatively large. It is also confirmed that the model-based clustering produces more reliable clustering than similarity based ones.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.547-549
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• 2012

Forex trading participants, due to the intensified economic internationalization exchange risk avoidance measures are needed. In this research, Model suitable for estimation of time-series data, such as stock prices and exchange rates, through the concealment of HMM and estimate the short-term exchange rate forecasting model is applied to the prediction of the future. Estimated by applying the optimal model if the real exchange rate data for a certain period of the future will be able to predict the movement aspect of it. Alleged concealment of HMM. For the estimation of the model to accurately estimate the number of states of the model via Bayesian Information Criterion was confirmed as a model predictive aspect of physical exercise aspect and predict the movement of the two curves were similar.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.139-144
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• 2011

Most real world system such as world economy, management, medical and engineering applications contain a series of complex phenomena. One of common methods to understand these system is to build a model and analyze the behavior of the system. As a first step, Determining the best clusters on data. As a second step, Determining the model of the cluster. In this paper, we investigated heuristic search methods for efficient clustering. It is also confirmed that the Bayesian Information Criterion more reliable than Cheeseman-Stutz ones.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.23-30
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• 2006

A clustering method for temporal data takes a model-based approach. This uses automata based model for each cluster. It is necessary to construct global models for a set of data in order to elicit individual models for the cluster. The preparation for building individual models is completed by determining the number of clusters inherent in the data set. In this paper, BIC(Bayesian Information Criterion) approximation is used to determine the number clusters and confirmed its applicability. A search technique to improve efficiency is also suggested by analyzing the relationship between data size and BIC values. A number of experiments have been performed to check its validity using artificially generated data sets. BIC approximation measure has been confirmed that it suggests best number of clusters through experiments provided that the number of data is relatively large.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.229-235
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• 2012

Forex trading participants, due to the intensified economic internationalization exchange risk avoidance measures are needed. In this research, Model suitable for estimation of time-series data, such as stock prices and exchange rates, through the concealment of HMM and estimate the short-term exchange rate forecasting model is applied to the prediction of the future. Estimated by applying the optimal model if the real exchange rate data for a certain period of the future will be able to predict the movement aspect of it. Alleged concealment of HMM. For the estimation of the model to accurately estimate the number of states of the model via Bayesian Information Criterion was confirmed as a model predictive aspect of physical exercise aspect and predict the movement of the two curves were similar.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.