• 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.

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.207-218
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• 2010

The purpose of the study is to predict the shipping business during the period of 2011 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2010. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors of all the ARIMA-type models are somewhat higher than normally expected. Furthermore, the random walk model outperforms all the ARIMA-type models. This reveals that the BDI is just a random walk phenomenon and it's meaningless to predict the BDI using various econometric techniques. The ARIMA-type models show that the shipping market is expected to be bearish in 2011. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

• 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.

• 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.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.785-799
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• 2018

With the development of renewable energy sector, the importance of solar energy is continuously increasing. Solar radiation forecasting is essential to accurately solar power generation forecasting. In this paper, we used time series models (ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH). We compared the performance of the models using mean absolute error and root mean square error. According to the performance of the models without exogenous variables, the Seasonal ARIMA-GARCH model showed better performance model considering the problem of heteroscedasticity. However, when the exogenous variables were considered, the ARIMAX model showed the best forecasting accuracy.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.69-84
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• 2015

The purpose of this study is to forecast the seaborne trade volume during January 1994 to December 2014 using the multiplicative seasonal autoregressive integrated moving average (ARIMA) along with intervention factors and an artificial neural network (ANN) model. Diagnostic checks of the ARIMA model were conducted using the Ljung-Box Q and Jarque-Bera statistics. All types of ARIMA process satisfied the basic assumption of residuals. The ARIMA(2,1,0) $(1,0,1)_{12}$ model showed the lowest forecast error. In addition, the prediction error of the artificial neural network indicated a level of 5.9% on hidden layer 5, which suggests a relatively accurate forecasts. Furthermore, the ex-ante predicted values based on the ARIMA model and ANN model are presented. The result shows that the seaborne trade volume increases very slowly.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.1-23
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• 2013

This paper estimates and forecasts the container throughput of Busan port using the monthly data for years 1992-2011. To do this, this paper uses the several seasonal multiplicative ARIMA models. Among several ARIMA models, the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$ is selected as the best model by AIC, SC and Hannan-Quin information criteria. According to the forecasting values of the selected seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$, the container throughput of Busan port for 2013-2020 will increase steadily annually, but there will be some volatile variations monthly due to the seasonality and other factors. Thus, to forecast the future container throughput of Busan port and to develop the Busan port efficiently, we need to use and analyze the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$.

• Journal of Korea Port Economic Association
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• v.27 no.4
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• pp.1-11
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• 2011

In much the same way as the US Lehman crisis of 2008-2009 severely impacted the European economy through financial market dislocation, a European banking crisis would materially impact the US economy through a generalized increase in global risk aversion. A deepening of the European crisis could very well derail the US economic recovery and have a harmful impact on the Asian economies. This kind of vicious circle could be a bad news to the shipping companies. The purpose of the study is to predict the Baltic Dry Index representing the shipping business during the period of 2012 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2011. The out-of-sample forecasting performance is also calculated. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors, however, are somewhat higher than normally expected. This reveals that it is very difficult to predict the BDI The ARIMA-type models show that the shipping market will be bearish in 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.73-77
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• 2005

본 논문은 시계열에 내재된 설${\cdot}$추석 등 음력 명절효과의 존재를 검정하기 위해 RegARIMA 모형의 잔차에 대한 t-검정 통계량을 제시하였으며 Box-plot에 의한 그래프적 진단을 시도하였다. 제시된 t-검정 결과를 X-12-ARIMA의 AICC-사전검정 및 RegARIMA 모형에 의해 추정된 명절효과 회귀계수의 t-값과 비교하였다. 사용된 명절효과 변수는 Bell과 Hillmer(1983)의 명절효과 변수이다.