• 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.

• Journal of Applied Reliability
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• v.3 no.1
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• pp.13-40
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.231-242
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• 2016

X-13-ARIMA (a popular time series analysis software) provides $3{\times}3$, $3{\times}5$, $3{\times}9$, $3{\times}15$ moving average filters for seasonal adjustment. However, there has been questions on their performance and the need for new filters is a constant topic due to Korean economic time series often containing higher irregularity and more various seasonality than other countries. In this study, two newly developed seasonal moving average filters, $3{\times}7$ and $3{\times}11$, are introduced. New filters were implemented in X-13-ARIMA and applied to 15 economic time series to demonstrate their suitability and reliability. The result shows that some series are more stable when using new seasonal moving average filters. More accurate time series analyses would be possible if newly proposed filters are used together with existing filters.

• Korean Management Science Review
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• v.20 no.1
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.997-1021
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• 2010

This paper compares the two most widely used seasonal adjustment methods: the X-12-ARIMA and TRAMO-SEATS procedures. The basic features of these methods are discussed and compared in both their theoretical and empirical aspects. In doing so, the X-13A-S program is used to reevaluate their applicability to Korean macroeconomic data by considering possible structural breaks in the series. The finding is that both methods provide very reliable and stable estimates of seasonal factors and seasonally adjusted data. As for the empirical comparisons, TRAMO-SEATS appears to outperform X-12-ARIMA, although the results are somewhat mixed depending on the comparison criteria used and on the series under analysis. In particular, the performance of TRAMO-SEATS turns out to compare more favorably when seasonal adjustment is carried out to each sub-samples (by taking possible structural breaks into account) than when the whole sample period is used. The result suggests that as the model-based TRAMO-SEATS has a considerable theoretical appeal, some features of TRAMO-SEATS should further be incorporated into X-12-ARIMA until a standard and integrated procedure is reached by combining the theoretical coherence of TRAMO-SEATS and the empirical usefulness of X-12-ARIMA.

• Journal of Preventive Medicine and Public Health
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• v.16 no.1
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• pp.113-120
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• 1983

The main objective of this case study is to develop demand forecasting model for durg inventory control in a university hospital. This study is based on the pertinent records during the period of January 1975 to August 1981 in the pharmacy and stock departments of the hospital. Through the analysis of the above records the author made some major findings as follows: 1. In A.B.C. classification, the biggest demand (A class) consists of 9 items which include 6 items of antibiotics. 2. Demand forecasting level of an index or discrepancy in A class drug compared with real demand for 6 months is average 30.4% by X-11 Arima method and 84.6% by Winter's method respectively. 3. After the correcting ty the number of bed, demand forecasting of drug compared with real demand for 6 months is average 23.1% by X-11 Arima method and 46.6% by Winter's method respectively.