• Journal of the Korean Society of Safety
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• v.29 no.2
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• pp.76-81
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• 2014

Railroad traffic accident consists of train accident, level-crossing accident, traffic death and injury accident caused by train or vehicle, and it is showing a continuous downward trend over a long period of time. As a result of the frequency comparison of train accidents and level-crossing accidents using the railway accident statistics data of Railway Industry Information Center, the share of train accident is over 90% in the 1990s and 80% in the 2000s more than the one of level-crossing accidents. In this study, we investigated time series characteristic and short-term prediction of railroad crossing, as well as seasonal characteristic. The analysis data has been accumulated over the past 20 years by using the frequency data of level-crossing accident, and was used as a frequency data per month and year. As a result of the analysis, the frequency of accident has the characteristics of the seasonal occurrence, and it doesn't show the significant decreasing trend in a short-term.

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2B
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• pp.217-227
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• 2004

With a rapid growth in the Internet technology, the network traffic is increasing swiftly. As for the increase of traffic, it had a large influence on performance of a total network. Therefore, a traffic management became an important issue of network management. In this paper, we study a forecast plan of network traffic in order to analyze network traffic and to establish efficient correspondence. We use time series forecast models and determine fitness whether the model can forecast network traffic exactly. In order to predict a model, AR, MA, ARMA, and ARIMA must be applied. The suitable model can be found that can express the nature of traffic for the forecast among these models. We determines whether it is satisfied with stationary in the assumption step of the model. The stationary can get the results by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). If the result of this function cannot satisfy then the forecast model is unsuitable. Therefore, we are going to get the correct model that is to satisfy stationary assumption. So, we proposes a way to classify in order to get time series materials to satisfy stationary. The correct prediction method is managed traffic of a network with a way to be better than now. It is possible to manage traffic dynamically if it can be used.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.66-76
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• 2019

Recently, natural disasters such as floods and droughts are frequently occurred due to climate change and the damage is also increasing. Wetland is known to play an important role in reducing and minimizing the damage. In particular, water level variability needs to be analyzed in order to understand the various functions of wetland as well as the reduction of damage caused by natural disaster. Therefore, in this study, we fitted water level series of Upo wetland in Changnyeong, Gyeongnam province to a proper time series model and residual test was performed to confirm the appropriateness of the model. In other words, ARIMA model was constructed and its residual tests were performed using existing nonparametric statistics, BDS statistic, and Close Returns Histogram(CRH). The results of residual tests were compared and especially, we showed the applicability of CRH to analyze the residuals of time series model. As a result, CRH produced not only accurate randomness test result, but also produced result in a simple calculation process compared to the other methods. Therefore, we have shown that CRH and BDS statistic can be effective tools for analyzing residual in time series model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.227-242
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• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.21-32
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• 2023

The amount of money processed in medicine from the Korea Consumer Agency was studied by the various time series models. The medical data set from the Korea Consumer Agency were consisted of counseling, damage relief and conciliation. For the analysis of time series, autoregressive moving average model, vector autoregressive model and the transfer function model were used. We considered the stationarity and cross correlation function for the identification and fitting. As a result, the transfer function model showed a better prediction. Whereas, the vector autoregressive model also provided good information for the degree and duration of the influence of variables.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.49-56
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• 2022

ROK Navy intends to secure the Korean-type aircraft carrier in order to effectively prepare for various future security threats. In general, the Korean national competency is considered to be at the level of having an aircraft carrier, but it is unclear what scale aircraft carrier would be appropriate. In this study, the efficiency was evaluated through the relative comparison between national competency(national power, economic power) and the scale of aircraft carriers, and the optimal scale of the Korean-type aircraft carrier that could be acquired was presented. A DEA(Data Envelopment Analysis) model was applied to aircraft carriers(19 aircraft carriers in 11 countries) currently in operation and scheduled to be possessed in the world. As input variables, CINC(Composite Index of National Capability) and GDP(Gross Domestic Product), which are the most widely used as indicators of national and economic power, and as output variables, the full-load displacement, length, and width of aircraft carriers were selected. ARIMA(short-term within 5 years) and simple regression(long-term over 5 years) were used to estimate the future national competency of each country at the time of aircraft carriers acquisition. The relative efficiency score of the Korean-type aircraft carrier currently being evaluated is 1.062, and it was evaluated as small-scale aircraft carrier compared to the national competency. Based on Korean national competency, the optimal scale of the Korean-type aircraft carrier calculated by aggregating benchmark groups, is 58,308.1 tons of full-load displacement, 279.4m in length, and 68.3m in width.

• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.31-38
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• 2020

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.