• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1080-1083
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• 2000

In this paper, we show that a variation in sampling rate give rise to system performance degradation and propose a method to effectively reduce the error. We first capture the variation as a first order autoregressive (AR) model and project it as an additional sensor measurement noise. By considering that the sensor measurements include correlated noise, we perform a decorrelation process and then apply a standard Kalman filter (SKF) to estimate the target-state. As a result of the two-step procedure, we achieve a significant reduction in the target state estimation error.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.1063-1070
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• 2017

This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.56-61
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• 2017

This paper presents a data prediction method and its application to haptic-based teleoperation systems. In general, time delays inevitably occur during data transmission in a network environment, which degrades the overall performance of haptic-based teleoperation systems. To address this situation, this paper proposes an autoregressive moving average (ARMA) model-based data prediction algorithm for estimating model parameters and predicting future data recursively in real time. The proposed method was applied to haptic data captured every 5 ms while bilateral haptic interaction was carried out by two users with an object in a virtual space. The results showed that the prediction performance of the proposed method had an error of less than 1 ms when predicting position-level data 100 ms ahead.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.2
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• pp.85-93
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• 2006

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. In an electricity market the short-term market price affects considerably the short-term trading between the market entities. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a new methodology for a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) model based on the time-series method. And also the correction algorithm is proposed to minimize the forecasting error in order to improve the efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the case studies are performed using historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4240-4258
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• 2016

A practical iterative channel estimation technique is proposed for the multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the high-speed mobile environment, such as high speed railway scenario. In the iterative algorithm, the Kalman filter and data detection are jointed to estimate the time-varying channel, where the detection error is considered as part of the noise in the Kalman recursion in each iteration to reduce the effect of the detection error propagation. Moreover, the employed Kalman filter is from the canonical state space model, which does not include the parameters of the autoregressive (AR) model, so the proposed method does not need to estimate the parameters of AR model, whose accuracy affects the convergence speed. Simulation results show that the proposed method is robust to the fast time-varying channel, and it can obtain more gains compared with the available methods.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.3
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• pp.364-372
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• 2007

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. In this article, two time series ARE models, the direct ARE model and applied ARE model have been considered for analyzing the ozone data at southern part of the Gyeonggi-Do, Pyeongtaek, Osan and Suwon monitoring sites in Korea. The result shows that the direct ARE model is better suited for describing the ozone concentration in all three sites. In both of the ARE models, eight meteorological variables and four pollution variables are used as the explanatory variables. Also the high level of ozone data (over 80 ppb) have been analyzed at the Pyeongtaek, Osan and Suwon monitoring sites.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.403-418
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• 2004

Recently space-time model in spatial data analysis is widly used. In this paper we applied this model to analysis of greenhouse whitefly. For handling time component, we used ARMA model and autoregressive error model and for outliers, we adapted Mugglestone's method. We compared space-time models and geostatistic model with MSE and MAPE.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.701-710
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• 2016

The purpose of this paper is to build a model of packet amount of Naver mobile portal. We collected 2004 cases by measuring the sixth per access from September, 2012 to October, 2015. We use regression model with autoregressive errors, in which predictors incorporated into the model were replication, date, time, week, month. It has been found the model which errors follow AR(36), based on AIC and adjusted $R^2$. We found some characteristics from our model as follows. In addition to model building, we also have discussed some meaningful features yielded from the selected model in this paper. Considering the importance of this topic, continuous researches are needed.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.91-102
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• 2007

The major purpose of this article is to formulate a class of Space Time Autoregressive Moving Average(STARMA) model and Space Time Bilinear model(STBL), to discuss some of the their statistical properties such as model, identification approaches, some procedure for estimation and the predictions, and to compare the STARMA model with the STBL model. For illustration, The Mumps data reported from eight city & provinces monthly over the years 2001-2006 are used and the result from STARMA and STBL model are compared with using SSF(Sum of Square Prediction Error).