• Journal of KIISE:Information Networking
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• v.37 no.4
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• pp.284-293
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• 2010

For the successful deployment of Mobile Internet, fast handover technologies are essential. For the past few years several handover mechanisms are suggested, and Fast Handover for Proxy Mobile IPv6 (PFMIPv6) is one of the promising schemes for this purpose. In this paper, we propose a novel L2/L3 cross layer handover scheme based on ARIMA prediction model to apply PFMIPv6 to Mobile Internet environment effectively. Performance gains are evaluated in terms of probabilities of predictive-mode operation, handover latencies, packet loss probabilities, and signaling costs. Three mobilities models are used for our simulation: Manhattan Model, Open Area Model, and Freeway Model. Simulation results show that the proposed scheme can increase probabilities of predictive-mode operation and reduce handover latency, packet loss probabilities, and signaling cost.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.197-207
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• 2018

SMEs manufacturing Factory, which are small-scale production systems of various types, mass-produce and sell products in order to meet customer needs. This means that the company has an excessive amount of material supply to reduce the loss due to lack of inventory and high inventory maintenance cost. And the products that fail to respond to the demand are piled up in the management warehouse, which is the reality that the storage cost is incurred. To overcome this problem, this paper uses ARIMA model, a time series analysis technique, to predict demand in terms of seasonal factors. In this way, demand forecasting model based on economic order quantity model was developed to prevent stock shortage risk. Simulation is carried out to evaluate the effectiveness of the development model and to demonstrate the effectiveness of the development model as applied to SMEs in the future.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.469-480
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• 2017

On April 1, 2004, KTX (Korea Train eXpress), the first HSR (High-Speed Rail) in Korea, was introduced to Gyeongbu Line. The introduction of the KTX service led to a change in the number of passengers for Gyeongbu Line. Previous studies have analyzed the pre and post-event changes of the intervening events by either simple statistics or intervention ARIMA analysis. However, the intervention ARIMA model has a limitation that several assumptions such as the occurrence time and the type of intervention events are necessary. To this end, this study analyzed the effects of intervention event on the number of passengers using the Gyeongbu line based on a time series outlier detection technique which can overcome limitations in the previous studies. The time series outlier detection technique can analyze the time, effect type and size of an intervention event without the assumption of the time and effect type of the intervention event. The data were collected from the Korea Transport Database (KTDB) for twelve years from 2003 to 2014 (144 months). The analysis results showed that the size of the influence type in the same intervention events was different across the major city routes, and the intervention event which could not be found by previous study methods was also found.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.26 no.4
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• pp.363-368
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• 1993

Short-term forecasting of fish catch is of practical importance in fisheries management. Ecosystem models and multi-species models as well as traditional single-species models fall short of predicting power needed for practical management of fisheries resources due to the lack of sufficient data or information for the required parameters. Univariate time series analysis, on the other hand, extracts the information on the stochastic variability from the time series itself and makes estimates of the future stochastic variability. Therefore, it can be used for short-term forecasting with minimum data requirements. ARIMA time series modeling has been applied to the monthly Korean catches of hairtail (Trichiurus lepturus) for $1971{\sim}1988$. Forecasts of hairtail catch were made and compared with the actual catch data from $1989{\sim}1990$ which were not included in the parameter estimation. The results showed a good agreement (r=0.938) between the forecasts and the actual catches with a mean rotative error of $59.5\%$

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.201-207
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• 2023

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.325-326
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• 2021

LNG는 환경규제에 따라 화석에너지에서 친환경 재생에너지로 전환되는데 중요한 역할을 하는 에너지원이다. UN산하 세계해사기구(IMO)의 MARPOL협약에 따라 선박 황산화물 배출가스규제로 LNG추진 선박에 대한 수요가 증가되고 있을 뿐만 아니라 미국의 쉐일혁명으로 LNG를 수출함에 따라 공급의 변화가 급격하게 이뤄지고 있다. 과거 국가 주도의 프로젝트 성격이 강한 LNG 운송시장은 장기정기용선계약이 대부분이었으나 수요와 공급시장의 급격한 변화로 스팟시장의 중요성이 커지고 있다. 따라서 본 논문은 LNG 운송시장에서 시장참여자들의 스팟거래에 합리적인 의사결정이 이뤄지도록 과학적인 예측방법을 제시하고자 한다. LNG 스팟운임 예측에 기계학습모델 중 인공신경망 모델을 적용할 것이며 기존의 시계열분석 방법인 ARIMA모델과 비교하여 본문에서 제시된 모델의 예측성능의 우수성을 확인하였다. 본 논문은 LNG 스팟운임을 다룬 최초의 연구로서 학문적인 차별성이 기대된다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.111-115
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• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.474-476
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• 2021

전용회선은 두 지역을 독점적으로 사용하는 구조이기 때문에 안정된 품질수준과 보안성이 확보되어 교환 회선의 급격한 증가에도 불구하고 지속적으로 많이 사용하는 회선 방식이다. 하지만 비용이 상대적으로 고가이기 때문에 네트워크 전용회선의 자원을 적절히 배치하고 활용하여 최적의 상태를 유지하는 것이 중요한 요소이다. 이에 본 연구에서는 기업 네트워크에서 사용하는 전용회선의 실제 사용률 데이터를 기반으로 다양한 시계열 데이터 예측 모델을 적용하고 성능을 평가하였다. 일반적으로 통계적인 방법으로 많이 사용하는 평활화 모형 및 ARIMA 모형과 요즘 많은 연구가 되고 있는 인공신경망에 기반한 딥러닝의 대표적인 모델들을 적용하여 각각의 예측에 대한 성능을 측정하고 비교하였다.

• 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.