• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1516-1519
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• 2011

예측에 필요한 중요한 자료에는 비선형 자료와 시계열과 같은 선형 자료 등이 있다. 이들 자료는 그 함축적인 관계가 매우 복잡하여 전통적인 통계분석 도구로 식별하는데 어려움이 많다. 신경망 분석은 비모수적 문제나 비선형 곡선 적합능력의 우수성 때문에 현실세계에서의 고유한 복잡성을 다루는 많은 경제 응용 분야에서 널리 이용되고 있다. 신경망은 또한 경제 시계열자료의 예측분야에서도 여러 연구에서 훌륭한 도구로서 적용되고 있다. 전통적으로 우리나라에서 시계열자료의 예측은 선형 자료적 분석을 중심으로 하는 분석도구인 자기회귀누적이동평균(ARIMA)모형을 이용한 시계열분석이 일반적이다. 이 연구에서는 신경망과 ARIMA 모형을 이용하여 한국의 주가변동 자료 및 자동차등록 현황 자료등과 같은 시계열자료를 이용한 예측결과를 비교한다. 연구의 결과는 신경망을 이용한 예측 방법들이 ARIMA 예측 결과보다 통계적으로 작은 오차를 주는 보다 효율적인 방법임을 보여주고 있다.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Environmental Engineering Research
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• v.20 no.3
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• pp.268-276
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• 2015

Solid waste production increases due to population and consumption increments. Landfill is the ultimate destination for all kinds of municipal solid waste; and is the most convenient waste disposal method in developing countries. To minimize investment and operational costs and society's opposition towards locating landfills nearby, proper landfill sizing and siting are essential. In this study, solid waste forecasting using Autoregressive Integrating Moving Average (ARIMA) was integrated with government future plans and waste composition to estimate the required landfill area for the state of Selangor, Malaysia. Landfill siting criteria were then prioritized based on expert's preferences. To minimize ambiguity and the uncertainty of the criteria prioritizing process, the expert's preferences were treated using integrated Median Ranked Sample Set (MRSS) and Analytic Hierarchy Process (AHP) models. The results show that the required landfill area is 342 hectares and the environmental criteria are the most important; with a priority equal to 48%.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.111-120
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• 2011

High-rise buildings have recently increased over the residential, commercial and office facilities, thus an understanding of construction cost for high-rise building projects has been a fundamental issue due to enormous construction cost as well as unpredictable market conditions and fluctuations in the rate of inflation by long-term construction periods of high-rise projects. Especially, recent violent fluctuations of construction material prices add to problems in construction cost forecasting. This research, therefore, develops a time-series model with the Box-Jenkins methodologies and material prices time-series data in Korea in order to forecast future trends of unit prices of required materials. BIM (Building Information Modeling) approaches are also used to analyze injection time of construction resources and to conduct quantity takeoff so that total material price can be forecasted. Comparative analysis of Predictability of tentative ARIMA (Autoregressive Integrated Moving Average) models was conducted to determine optimal time-series model for forecasting future price trends. Proposed BIM based time series forecasting model can help to deal with sudden changes in economic conditions by estimating future material prices.

• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.62-67
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• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1257-1264
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• 2018

This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.207-214
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• 2009

This study proposed an advanced warning information system based on real-time traffic conflict analysis. An algorithm to detect and analyze unsafe traffic events associated with car-following and lane-changes using individual vehicle trajectories was developed. A positive guidance procedure was adopted to provide warning information to alert drivers to hazardous traffic conditions derived from the outcomes of the algorithm. In addition, autoregressive integrated moving average (ARIMA) analyses were conducted to investigate the predictability of warning information for the enhancement of information reliability.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.4
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• pp.529-540
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• 2018

As the equipment of the military has recently become more sophisticated and expensive, the cost of purchasing spare parts is also steadily increasing. Therefore, demand forecast accuracy is also becoming an issue for the effective execution of the spare parts budget. This study predicts the demand by using the data of spare parts consumption of the KF-16C fighter which is being operated in the Republic of Korea Air Force. In this paper, SARIMA(Seasonal Autoregressive Integrated Moving Average) is applied to seasonal data after dividing the spare parts consumptions into seasonal data and non-seasonal data. Proposing new methods, Majority Voting and Hybrid Method, to the non-seasonal data which consists of spare parts of low consumption with unclear pattern, We want to prove that the demand forecast accuracy of spare parts improves.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.50-56
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• 2020

In this study, we investigated whether long short-term memory (LSTM) can be used in the future to predict F10.7 index data; the F10.7 index is a space environment factor affecting atomic oxygen erosion. Based on this, we compared the prediction performances of LSTM, the Autoregressive integrated moving average (ARIMA) model (which is a traditional statistical prediction model), and the similar pattern searching method used for long-term prediction. The LSTM model yielded superior results compared to the other techniques in the prediction period starting from the max/min points, but presented inferior results in the prediction period including the inflection points. It was found that efficient learning was not achieved, owing to the lack of currently available learning data in the prediction period including the maximum points. To overcome this, we proposed a method to increase the size of the learning samples using the sunspot data and to upgrade the LSTM model.

• Journal of Korean Society of Rural Planning
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• v.17 no.1
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• pp.1-11
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• 2011

본 연구에서는 ARIMA(Autoregressive Integrated Moving Average) 모델을 이용하여 농촌관광마을의 월별 관광객을 추정하였다. 단일 마을에 대한 시계열 자료를 경상북도 안동시에 위치한 하회마을을 대상으로 구축하였다. 월별 시계열 자료는 2000년부터 2010년까지 구성되었는데(2008년도 누락), 2000년에서 2007년까지 자료는 최적 모델의 도출에 나머지는 예측치의 검정에 사용되었다. 연구 결과 최적모델에 필요한 시계열 자료의 길이는 6년으로 나타났으며, 최적모델은 계절성을 고려한 SARIMA(2,1,1)(1,1,2)12로 나타났다. 최적 시계열 년수로 나타난 6년을 사용하여 2000-2005, 2001-2006, 그리고 2002-2007의 자료로부터 각각 SARIMA(2,1,1)(1,1,2)12를 도출하여, 차기년도들에 대한 예측결과를 비교한 결과, 높은 $R^2$값을 보였다.