• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.133-146
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• 2014

The United States Census Bureau released a new version of X-13ARIMA-SEATS that integrates X-12-ARIMA with TRAMO-SEATS. This paper compares a seasonal adjusted series from X-13ARIMA-SEATS and those from X-12-ARIMA. An X11 filter and SEATS filter were used for the X-13ARIMA-SEATS. The result of the comparison suggests that seasonal adjusted series using X-13ARIMA-SEATS with the X11 filter are similar to those of X-12-ARIMA.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.335-350
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• 1998

X11ARIMA is established on the basis of X11 which is one of smoothing approach in time series area and this procedure was introduced by Bureau of Census of United States and developed by Dagum(1975). This procedure had been updated and adjusted by Dagum(1988) with 174 economic index of North America and has been used until nowadays. Recently, X12ARIMA procedure has been studied by William Bell et.al. (1995) and Chen. & Findly(1995) whose approaches adapt adjusting outliers, Trend-change effects, seasonal effect, arid Calender effect. However, both of these procedures were implemented for correct adjusting the economic index of North America. This article starts with providing some appropriate and effective ARIMA model for 102 indexes produced by national statistical office in Korea; which consists of production(21), shipping(27), stock(27), and operating rate index(21). And a reasonable smoothing method will be proposed to reflect the specificity of Korean economy using several moving average model. In addition, Sulnal(lunar happy new year) and Chusuk effects will be extracted from the indexes above and both of effects reflect contribution of lunar calender effect. Finally, we will discuss an alternative way to estimate holiday effect which is similar to X12ARIMA procedure in concept of using both of ARIMA model and Regression model for the best fitness.

• International Area Studies Review
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• v.21 no.4
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• pp.159-172
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• 2017

In a typical trend-cycle decomposition of GDP, the trend component is usually assumed to follow a random walk process. This paper considers an ARIMA trend and assesses the validity of the ARIMA trend model. I construct univariate and bivariate unobserved-components(UC) models, allowing the ARIMA trend. Estimation results using U.S. data are favorable to the ARIMA trend models. I, also, compare the forecasting performance of the UC models. Dynamic pseudo-out-of-sample forecasting exercises are implemented with recursive estimations. I find that the bivariate model outperforms the univariate model, the smoothed estimates of trend and cycle components deliver smaller forecasting errors compared to the filtered estimates, and, most importantly, allowing for the ARIMA trend can lead to statistically significant gains in forecast accuracy, providing support for the ARIMA trend model. It is worthy of notice that trend shocks play the main source of the output fluctuation if the ARIMA trend is allowed in the UC model.

• Convergence Security Journal
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• v.8 no.2
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• pp.35-40
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• 2008

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. The used software failure time data for forecasting failure time is random number of Weibull distribution(shaper parameter 1, scale parameter 0.5), Using this data, we are proposed to ARIMA(AR(1)) and simulation method for forecasting failure time. The practical ARIMA method is presented.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.147-160
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• 2020

In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.56-58
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• 2019

배터리의 효율적인 관리와 안정적인 운영을 위해서는 배터리의 노화에 따른 배터리의 모니터링이 필요하다. 하지만 모델 기반의 SOH 예측 모델의 경우 파라미터의 변화에 대한 정확한 정보가 반영되지 않을 경우 심각한 오류를 야기 할 수 있다. 따라서 본 논문에서는 비 모델인 시계열 예측 기법 ARIMA 모델을 제안하고 전기적 특성 실험을 통한 내부 파라미터에 대한 분석과 파라미터에 대한 상관분석, 이를 통한 SOH 예측을 통해 ARIMA 모델의 특성 및 정확성에 대해 제안한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3381-3388
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• 2012

This research is to find out the method appropriate for the forecasting of passennger demand using seasonal ARIMA model and efficient operation in Jeju National Airport. Time series monthly data for the investigation were collected ranging from January 2003 to December 2011. A total of 108 observations were used for data analysis. Research findings showed that the multiplicative seasonal ARIMA(0.1.2)(0.1.1)12 model is appropriate model. The number of passengers in Jeju National Airport will continue to rise, it was expected to surpass 20 million people.

• Journal of agriculture & life science
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• v.45 no.5
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• pp.115-126
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• 2011

This paper estimates the number of Korean cattle using time-series ARIMA model. This study classifies the structure of the number of cattle into six indexes to reflect the characteristics of cattle. This study apply ARIMA model to these six indexes according to Box-Jenkins procedure to identify, estimate and predict. The rates of slaughter for aged female and aged male cow is analyzed as non-stationary time series which has unit roots and other 4 indexes is analyzed as stationary time series. The differencing is applied to get rid of non-stationarity for the non-stationary time series. The results show that the number of cattle will be reduced from 2012 as a higher point and rebounded from 2018 as a lower point.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.124-130
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• 2011

This study was conducted to find out appropriate model and forecast visit demand of korea national parks using seasonal ARIMA model. Data of monthly visitors uses of 18 korea national parks from January, 2003 to December, 2010 was used to analyze. The result showed that $ARIMA(1,0,0)(1,1,0)_{12}$ model was selected as a appropriate model to forecast visit demand of korea national parks and the result of post evaluation used by index of mean absolute percentage error was accurate. Therefore, the result of this study will enhance reliability and validity of forecasting technique and contribute to management strategy of korea national park.

• Journal of the Korean GEO-environmental Society
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• v.20 no.10
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• pp.29-38
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• 2019

In stability and settlement management of soft ground, the settlement prediction technology has been continuously developed and used to reduce construction cost and confirm the exact land use time. However, the preexistence prediction methods such as hyperbolic method, Asaoka method and Hoshino method are difficult to predict the settlement accurately at the beginning of consolidation because the accurate settlement prediction is possible only after many measurement periods have passed. It is judged as the reason for estimating the future settlement through the proportionality assumption of the slope which the preexistence prediction method computes from the settlement curve. In this study, ARIMA technique is introduced among time series analysis techniques and compared with preexistence prediction methods. ARIMA method was predictable without any distinction of ground conditions, and the results similar to the existing method are predicted early (final settlement).