• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.151-162
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• 2002

The object of this study is to develop an operating time prediction model for expressways using toll collection data. A Prediction model based on modular neural network model was developed and tested using real data. Two toll collection system(TCS) data set. Seoul-Suwon section for short range and Seoul-Daejeon section for long range, in Kyongbu expressway line were collected and analyzed. A time series analysis on TCS data indicated that operating times on both ranges are in reasonable prediction ranges. It was also found that prediction for the long section was more complex than that for the short section. However, a long term prediction for the short section turned out to be more difficult than that for the long section because of the higher sensitivity to initial condition. An application of the suggested model produced accurate prediction time. The features of suggested prediction model are in the requirement of minimum (3) input layers and in the ability of stable operating time prediction.

• Water for future
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• v.9 no.2
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• pp.87-100
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• 1976

These studies are aimed at the analysis of systematic variation pattern of water resources in Korean river catchments and the development of their simulation models from the stochastic analysis of monthly and annual hydrologic data as main elements of water resources, i.e. rainfall and streamflow. In the analysis, monthly & annual rainfall records in Soul, Taegu, Pusan and Kwangju and streamflow records at the main gauging stations in Han, Nakdong and Geum river were used. Firstly, the systematic variation pattern of annual streamflow was found by the exponential function relationship between their standard deviations and mean values of log-annual runoff. Secondly, stochastic characteristics of annual rainfall & streamflow series were studied by the correlogram Monte Carlo method and a single season model of 1st-order Markov type were applied and compared in the simulation of annual hydrologic series. In the simulation, single season model of Markov type showed better results than LN-model and the simulated data were fit well with historical data. But it was noticed that LN-model gave quite better results in the simulation of annual rainfall. Thirdly, stochastic characteristics of monthly rainfall & streamflow series were also studied by the correlogram and spectrum analysis, and then the Model-C, which was developed and applied for the synthesis of monthly perennial streamflow by lst author and is a Markov type model with transformed skewed random number, was used in the simulation of monthly hydrologic series. In the simulation, it was proved that Model-C was fit well for extended area in Korea and also applicable for menthly rainfall as well as monthly streamflow.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.63-89
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• 2007

This study aims at empirically verifying whether long memory properties exist in returns and volatility of the financial time series and then, empirically observing influential factors of long-memory properties. The presence of long memory properties in the financial time series is examined with the Hurst exponent. The Hurst exponent is measured by DFA(detrended fluctuation analysis). The empirical results are summarized as follows. First, the presence of significant long memory properties is not identified in return time series. But, in volatility time series, as the Hurst exponent has the high value on average, a strong presence of long memory properties is observed. Then, according to the results empirically confirming influential factors of long memory properties, as the Hurst exponent measured with volatility of residual returns filtered by GARCH(1, 1) model reflecting properties of volatility clustering has the level of $H{\approx}0.5$ on average, long memory properties presented in the data before filtering are no longer observed. That is, we positively find out that the observed long memory properties are considerably due to volatility clustering effect.

• Journal of International Area Studies (JIAS)
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• v.22 no.1
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• pp.91-112
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• 2018

This study examined the dynamic relationship between urbanization and energy consumption in China. As an alternative to the conventional method of having the same integration of time series and large samples, ARDL method and Toda-Yamamoto causality analysis were applied. As a result, urbanization income, income, and energy consumption have a long-term stable equilibrium. Urbanization and income have a positive effect on energy consumption in the long run, but short-term changes of urbanization and income have no significant effect on energy consumption changes. The adjusted coefficient was -0.2395, which was statistically significant. In the causality test, income and energy consumption are useful to predict each other, but urbanization is exogenous because there are no causality with other variables. Since the process of urbanization in China has been proceeding slowly and deliberately by the government, it can be seen that the long-term effects of urbanization are clear and exogenous.

• 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.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.456-463
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• 2018

Increases in disasters and damage caused by the disasters in modern society, have a negative impact on local economy. In particular, a local economic downturn leads to a deterioration in quality of life of local residents and causes mental and material damage. Therefore, in order to achieve stable and sustainable local economic development, it is necessary to strengthen the resilience of the local economy. This study aims to estimate indicators of local economic resilience of Jindo County after the Sewol Ferry disaster, analyze a trend of the economic level after the disaster through time series data and suggest improvement plans of the local crisis management and restoration policy that considers future economic resilience. Results of this study showed that a decrease in the number of tourists and of workers in related industries hit tourism industry, causing a loss to the local economy and that an increase in a drinking rate of and stress awareness rate of local residents was a stress factor due to disaster impacts. These findings provides policy implications that it is necessary to make efforts for improving the depressed local image by utilizing local resources in the area, to build a sustainable long-term economic recovery policy and to provide psychological treatment and the relevant government and local government's support for relieving the stress of local residents due to the disaster impacts.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.49-67
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• 2006

This study examines whether won/dollar futures have price discovery function and volatility spillover effect or not, using intraday won/dollar futures prices, volumes, and spot rates for the interval from March 2, 2005 through May 30, 2005. Futures prices and spot rates are non-stationary, but there is the cointegration relationship between two time series. Futures returns, spot returns, and volumes are stationary. Asymmetric effects on volatility in futures returns and spot returns does not exist. Analytical results of mean equations of the BGARCH-EC (bivariate GARCH-error correction) model show that the increase of futures returns raise spot returns after 5 minutes, which implies that futures returns lead spot returns and won/dollar futures have price discovery function. In addition, the long-run equilibrium relationship between the two returns could help forecast spot returns. Analytical results of variance equations indicate that short-run innovations in the futures market positively affect the conditional variances of spot returns, that is, there is the volatility spillover effect in the won/dollar futures market. A dummy variable of volumes does not have an effect on two returns but influences significantly on two conditional variances.

• Korean Institute of Interior Design Journal
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• no.3
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• pp.91-99
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• 1994

The purpose of this study was to determine the long term effect of a pleasantly designed interior on pro-saptial behavior. For pleasantly designed interior, the existing interior was remodeled through the change of finishing materials for major architectural elements such as wall, floor and ceiling, and changes of furniture and it's arrangement . Pro-spatial behavior was operationalized as seat arranging behavior and measured through the arranged condition and observable arranging behavior. Time-series design, one of quasi-experimental design was used. The data in this study were extracted from an existing field experimental research. Five hundred survey video tapes record during 2 years period were used. In conclusion, the pleasantly designed environment has a long term effect on the pro-spatial behavior change . While self-centered pro-spatial was improved continuously and even reinforced , altruistic pro-spatial behavior was improved but diminished as time passed. There were no differences in the effect between male and female children. The result of the research provide scientific background of an answer to why Interior Design.

• Environmental and Resource Economics Review
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• v.21 no.2
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• pp.341-369
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• 2012

Population aging has been one of the serious problems in Korea. Aging can affect social and economic features including energy consumption. This paper analyzed how population aging makes an effect on residential electricity demand. Yearly data from 1965 to 2010 were collected. The long and short-run demands for residential electricity were estimated with respect to Korean aging index. The results show that population aging reduces residential electricity demands in the short run significantly, but the effect decreases in the long run. However, population aging still negatively affects residential electricity consumption in long run. If population keep aging as Korean government expected, then the residential electricity demand per capita will grow less than 3%.