• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Water for future
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• v.11 no.2
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• pp.54-61
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• 1978

An attemption of synthesizing and forecasting of monthly river flow has been made by employing a linear stochastic difference equation model. As one of the linear stochestic difference equation model, an ARIMA Type is tested to find the suitability of the model to the monthly river flows. On the assumption of the stationary covariacne of differenced monthly river flows the model is identrfield and is evaluated so that the residuale have the minimum variance. Finally a test is performed to finld the residerals beings White noise. Monthly river flows at six stations in Han River Basin are applied for case studies. It was found that the difference operator is a good measure of forecasting the monthly river flow.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.667-680
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• 2008

In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.2
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• pp.122-130
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• 2014

Seasonal variations and long term linear trends of SST (Sea Surface Temperature) at Yeosu Coast ($127^{\circ}37.73^{\prime}E$, $34^{\circ}37.60^{\prime}N$) in Korea were studied performing the harmonic analysis and the regression analysis of the monthly mean SST data of 46 years (1965-2010) collected by the Fisheries Research and Development Institute in Korea. The mean SST and the amplitude of annual SST variation show $15.6^{\circ}C$ and $9.0^{\circ}C$ respectively. The phase of annual SST variation is $236^{\circ}$. The maximum SST at Yeosu Coast occurs around August 26. Climatic changes in annual mean SST have had significant increasing tendency with increase rate $0.0305^{\circ}C/Year$. The warming trend in recent 30 years (1981-2010) is more pronounced than that in the last 30 years (1966-1995) and the increasing tendency of winter SST dominates that of the annual SST. The time series model that could be used to forecast the SST on a monthly basis was developed applying Box-Jenkins methodology. $ARIMA(1,0,0)(2,1,0)_{12}$ was suggested for forecasting the monthly mean SST at Yeosu Coast in Korea. Mean absolute percentage error to measure the accuracy of forecasted values was 8.3%.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.936-949
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• 2023

On August 3, 2023, a brutal incident of unprovoked violence, termed as "Abnormal motivated crime," occurred in a multi-use facility, where retail and transportation facilities converge, near Seohyeon Station. The assailant drove onto the sidewalk, hitting pedestrians, and then entered a department store where a knife rampage ensued, resulting in a total of 14 victims. In the aftermath of this incident, numerous murder threats were posted on social media, causing widespread anxiety among the public. This fear was further exacerbated by the emergence of a "Terrorless.01ab.net" service. Purpose: This research aims to explore necessary institutional improvements for private security personnel who protect customers and employees in multi-use facilities, to enable them to perform their duties more effectively. Method: To assess the risk of Abnormal motivated crime, a time series analysis using the ARIMA model was conducted to analyze the domestic trends of such crimes. Additionally, Result: the study presents suggestions for improvements in the domestic security service law and emergency manuals for multi-use facilities. Conclusion: This is informed by a legal analysis of the indemnity rights for weapon use by private security guards abroad and their operational authority beyond weapon usage.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.1-17
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• 2008

In this paper the cargo transportation were forecasted for the North Port in Busan through time series models. The cargo transportation were classified into three large groups; container, oil, general cargo. The seasonal indexes of existing cargo transportation were firstly calculated, and optimum models were chosen among exponential smoothing models and ARIMA models. The monthly cargo transportation were forecasted with applying the seasonal index in annual cargo transportation expected from the models. Thus, the cargo transportation in 2011 and 2015 were forecasted about 22,900 myriad ton and 24,654 myriad ton respectively. It was estimated that container cargo volume would play the role of locomotive in the increase of the future cargo transportation. On the other hand, the oil and general cargo have little influence upon it.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.82-89
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• 2014

Recently, the mineral resource protection policies and regulations in production countries of natural resources including rare metals are becoming more stringent. Such environment makes which market has malfunction. In other word, those are not perfect or pure market. Therefore because each market of natural resources have special or unique characters, it is difficult to forecast their market prices. In this study, we constructed several models to estimate prices of natural resources using statistical tools like ARIMA and their business indices. And for examples, Indium and Coal were introduced.

• Land and Housing Review
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• v.11 no.3
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• pp.11-19
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• 2020

This study aims to examine the relationship between the improvement of the income distribution index from the late 1980s to the 1990s and large-scale housing supply projects such as the 2 million housing construction project. Looking at Korea's economic development in terms of income growth and distribution, GDP has continuously increased since the establishment of the government, especially in the late 1980s. The Gini Index, a representative income inequality index, rapidly deteriorated in the early 1970s, and gradually improved from the late 1980s. The 2 million housing construction project, announced in 1988, supplied a third of the existing nationwide housing stock of 6.5 million units in three years. The project cost was 65 trillion won, equivalent to 50% of Korea's GDP at the time. This study questioned whether the ratio of the number of employed workers in the construction industry was a variable directly affecting the Gini Index. To verify this, the causal relationship between the proportion of employed workers in the construction and manufacturing industries and the Gini Index from 1979 to 2008 was statistically analyzed. For this, the ARIMA model was established for each variable, and the correlation of their residuals was verified. The 2 million housing construction project had the effect of improving income inequality in terms of rising wages for production workers and creating jobs for the low-educated and low-income class. During the project period, the number of middle-income earners increased sharply, and the income gap between the high-income and low-income earners greatly decreased. The expansion of the construction volume can be used as a powerful and direct policy tool for improving income distribution. However, the effect may be limited. When the proportion of workers exceeds the threshold, the effect is weakened.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2470-2478
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• 1999

In this paper, algorithm is proposed to forecast line utilization using SNMP MIB-II. We calculate line utilization using SNMP MIB-II on TCP/IP based Internet and suggest a method for forecasting a line utilization on the basis of past line utilization. We use a MA model taking difference transform among ARIMA methods. A system for orecasting is proposed. To show availability of this algorithm, some results are shown and analyzed about routers on real environments. We get a future line utilization using this algorithm and compare it ot real data. Correct results are obtained in case of being few data deviating from mean value. This algorithm for forecasting line utilization can give effect to line c-apacity plan for a manager by forecasting the future status of TCP/IP network. This will also help a network management of decision making of performance upgrade.