• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.181-193
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• 2017

The paper introduces bottom-up and optimal combination methods that can analyze and forecast hierarchical time series. These methods allow forecasts at lower levels to be summed consistently to upper levels without any ad-hoc adjustment. They can also potentially improve forecast performance in comparison to independent forecasts. We forecast regional traffic accident counts as time series data in order to identify efficiency gains from hierarchical forecasting. We observe that bottom-up or optimal combination methods are superior to independent methods in terms of forecast accuracy.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.1-13
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• 2022

This paper empirically explores the predicting ability of the newly proposed smooth transition (ST) time-varying combining forecast methods. The proposed method allows the "weight" of combining forecasts to change gradually over time through its unique feature of transition variables. Stock market returns from 7 countries were applied to Ad Hoc models, the well-known Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, and the Smooth Transition Exponential Smoothing (STES) models. Of the individual models, GJRGARCH and STES-E&AE emerged as the best models and thereby were chosen for constructing the combined forecast models where a total of nine ST combining methods were developed. The robustness of the ST combining forecasts is also validated by the Diebold-Mariano (DM) test. The post-sample forecasting performance shows that ST combining forecast methods outperformed all the individual models and fixed weight combining models. This study contributes in two ways: 1) the ST combining methods statistically outperformed all the individual forecast methods and the existing traditional combining methods using simple averaging and Bates & Granger method. 2) trading volume as a transition variable in ST methods was superior to other individual models as well as the ST models with single sign or size of past shocks as transition variables.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.111-121
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• 2004

With rapid growth of the Internet, network intrusions have greatly increased and damage of attacks has become more serious. Recently some kinds of Internet attacks cause significant damage to overall network performance. Current Intrusion Detection Systems are not capable of performing the real-time detection on the backbone network In this paper, we propose the broadband network intrusion detection system using the exponential smoothing method. We made an experiment with real backbone traffic data for 8 days. The results show that our proposed system detects big jumps of traffic volume well.

• Journal of Distribution Science
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• v.19 no.3
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• pp.25-34
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• 2021

Purpose: The purpose of this study is to analyze the economic ripple effect(ERE) of logistics industry by construction of Trans-Korea Railway (TKR) and present policy measures to minimize the economic loss of South Korea (SK). Research design, data and methodology: As the analysis method, exponential smoothing was used for demand forecasting, Input-Output analysis was used to estimate the economic ripple effect coefficient, and scenario analysis was used to an efficient way to invest in TKR to minimize SK's economic losses. Results: 1) the production(logistics fares) of TKR for 10 years after its completion is about 11.42 trillion won in positive relations, and 26.89 billion won in negative relations. 2) the ERE of SK in positive relations is 24.32 trillion won in production inducement effect, 8.1 trillion won in value-added inducement effect, 3.54 trillion won in import inducement effect, and 70,930 persons in employment inducement effect. But the ERE was insufficient in the negative relations. 3) SK's efficient investment method is providing materials and equipment by SK and building the TKR by North Korea in positive inter-Korea relations. Conclusions: For the successful operation of TKR, international cooperation, legalization and stable peace settlement on the Korean Peninsula are required.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.297-309
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• 2024

This paper evaluates the performance of the neural network autoregressive model combined with an exponential smoothing model, called the NNARX+ETS model. The combined model utilizes the components of ETS as exogenous variables for NNARX, to forecast time series data using artificial neural networks. The main idea is to enhance the performance of NNAR using only lags of the original time series data, by combining traditional time series analysis methods with the neural networks through NNARX. We employ two real data for performance evaluation and compare the NNARX+ETS with NNAR and traditional time series analysis methods such as ETS and ARIMA (autoregressive integrated moving average) models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.284-294
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• 2018

Statistical methods have been widely used to estimate the remaining battery runtime of mobile smart devices, such as smart phones, smart gears, tablets, and etc. However, existing work available in the literature only considers a particular statistical method. Thus, it is difficult to determine whether statistical methods are applicable to estimating thr remaining battery runtime of mobile devices or not. In this paper, we evaluated the performance of statistical methods applicable to estimating the remaining battery runtime of mobile smart devices. The statistical estimation methods evaluated in this paper are as follows: simple and moving average, linear regression, multivariate adaptive regression splines, auto regressive, polynomial curve fitting, and double and triple exponential smoothing methods. Research results presented in this paper give valuable data of insight to IT engineers who are willing to deploy statistical methods on estimating the remaining battery runtime of mobile smart devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.276-283
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• 2018

Climate changes consistently cause coastal accidents such as coastal flooding, so the studies on monitoring the marine environments are progressing to prevent and reduce the damage from coastal accidents. In this paper, we propose a new method to predict the sea level which can be applied to coastal monitoring systems to observe the variation of sea level and warn about the dangers. Existing sea level models are very complicated and need a lot of tidal data, so they are not proper for real-time prediction systems. On the other hand, the proposed algorithm is very simple but precise in short period such as one or two hours since we use the measured data from the sensor. The proposed method uses Kalman filter algorithm for harmonic analysis and double exponential smoothing for additional error correction. It is shown by experimental results that the proposed method is simple but predicts the sea level accurately.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.177-181
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• 2009

Developing u-Public facilities for application u-City is to combine both the state-of-the art of the construction and ubiquitous computing and must be flexibly comprised of the facilities for the basic service of the building such as air conditioning, heating, lighting and electric equipments to materialize a new format of spatial planning and the public facilities inside or outside. Accordingly, in this paper we suggested the time pattern system for predicting the most basic power system loads for the basic service. To application the tim e pattern we applied SOM algorithm and k-means method and then clustered the data each weekday and each time respectively. The performance evaluation results of suggestion system showed that the forecasting system better the ARIMA model than the exponential smoothing method. It has been assumed that the plan for power supply depending on demand and system operation could be performed efficiently by means of using such power load forecasting.

• 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.