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

• Journal of Environmental Health Sciences
• /
• v.28 no.3
• /
• pp.19-25
• /
• 2002

In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

• The Korean Journal of Applied Statistics
• /
• v.24 no.1
• /
• pp.93-102
• /
• 2011

In this paper, we compared the performance of the several time series models for tourism demand forecasting. We showed that seasonal effects in the data(Japan, China, USA, and Philippines) exist in the tourism data and the forecasting accuracies are compared by the RMSE criterion.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.161-171
• /
• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• Korea Trade Review
• /
• v.45 no.5
• /
• pp.75-89
• /
• 2020

The purpose of this paper was to analyze the trend of container volume using the Seasonal Autoregressive Intergrated Moving Average (SARIMA) model. To this end, this paper used monthly time-series data of the East Sea Rim from 2001 to 2019. As a result, the SARIMA(2,1,1)12 model was identified as the most suitable model, and the superiority of the SARIMA model was demonstrated by comparative analysis with the ARIMA model. In addition, to confirmed forecasting accuracy of SARIMA model, this paper compares the volume of predict container to the actual volume. According to the forecast for 24 months from 2020 to 2021, the volume of containaer increased from 60,100,000Ton in 2020 to 64,900,000Ton in 2021

• Journal of Korean Society of Transportation
• /
• v.31 no.3
• /
• pp.3-18
• /
• 2013

This study develops a forecasting method to estimate air cargo demand from ICN(Incheon International Airport) to all airports in EU with Seasonal Autoregressive Integrated Moving Average (SARIMA) Model using volumes from the first quarter of 2000 to the fourth quarter of 2009. This paper shows the superiority of SARIMA Model by comparing the forecasting accuracy of SARIMA with that of other ARIMA (Autoregressive Integrated Moving Average) models. Given that very few papers and researches focuses on air route, this paper will be helpful to researchers concerned with air cargo.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1037-1046
• /
• 2008

Recently, the oil suppliers who supply the tax-exempt oil to the fishery are confronted with big trouble in their supply and demand system due to the unstable global oil prices. We applied the seasonal ARIMA(SARIMA) model to the low-sulfur and high-sulfur crude oil which are in great request and developed forecasting systems for them. Since there are many parameters in SARIMA, it is difficult to estimate the optimal parameters, but it is overcome by using simulation looping program. In conclusion, we found that the obvious seasonality in demand of low-sulfur and these demands are tending downwards gradually.

• The Korean Journal of Applied Statistics
• /
• v.22 no.5
• /
• pp.1007-1017
• /
• 2009

Forecasting for air demand such as passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison the performance between the univariate seasonal ARIMA models and the multivariate time series models. In this paper, we used real data to predict demand on international passenger and freight. And multivariate time series models are better than the univariate models based on the accuracy criteria.

• The Korean Journal of Applied Statistics
• /
• v.36 no.5
• /
• pp.457-471
• /
• 2023

The monthly series is an aggregation of daily values. In the absence of observable daily data, calendar effects such as trading day and holidays are estimated using a RegARIMA model. However, if the daily series were observable, these calendar effects could be estimated directly from the daily series, potentially improving the seasonal adjustment of the monthly time series. In this paper, we propose a method to improve the seasonal adjustment of monthly time series by using calendar variation estimation based on daily time series. We apply this seasonal adjustment method to three monthly time series and compare our results with those obtained using X-13ARIMA-SEATS.

• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.1715-1721
• /
• 2011

For an efficient railroad operations the demand forecasting is required. Time series models can quickly forecast the future demand with fewer data. As well as the accuracy of forecasting is excellent compared to other methods. In this study is proposed the intervention ARIMA model for forecasting methods of KTX passenger demand. The intervention ARIMA model may reflect the intervention such as the Kyongbu high-speed rail project second phase. The simple seasonal ARIMA model is predicted to overestimate the KTX passenger demand. However, intervention ARIMA model is predicted the reasonable results. The KTX passenger demands were predicted to be a week units separated by the weekday and weekend.