• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.45-59
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• 2011

When using X-12-ARIMA for seasonal adjustment, we usually check whether the series has stable seasonality or not via D8 F-tests, Kruskal-Wallis test, and the spectral diagnostics. In this paper, we develop several smooth tests for seasonality based on a Fourier series to improve the spectral diagnostics of X-12-ARIMA. A simulation study is conducted to compare five smooth tests for seasonality and X-12-ARIMA's D8 F-test an Kruskal-Wallis test. The simulation study shows that smooth tests for seasonality performed well compared with D8 F-tests and a Kruskal-Wallis test.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.139-155
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• 2011

This study is to estimate the dynamic change of the regional railway passenger traffic and, based on the estimated, to forecast the future regional railway passenger traffic by using the Seasonal ARIMA model. The existing studies using ARIMA failed to consider seasonality nor the monthly or the quarterly data. It was attempted in this study to use the monthly regional railway passenger traffic data to propose a model that estimates dynamic change of demand. The authors employed the Seasonal ARIMA model previously developed and used (1) the numbers of monthly passenger data and (2) the monthly passenger-km data. The test results showed that the numbers of passengers in 2015 and 2020 would increase by 36% and 71%, respectively, compared to those in 2008. The numbers of passenger-kms in 2015 and 2020 would increase by 25% and 78%, respectively, compared to those in 2008.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.139-144
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• 2002

계절변동조정방법인 X-12-ARIMA방법을 이용할 때에는 우리 실정에 적합한 옵션을 선택하고, 우리만에 특수한 명절과 조업일수영향을 사전에 조정해야한다. 본고에서는 명절과 조업일수영향을 측정하는 모형을 설정하고, 이것으로 추정된 사전조정요인을 원계열에서 제거했을 때 계절변동 및 계절변동조정계열의 안정성이 향상되었는가를 진단하고, 분류별로 적합한 X-12-ARIMA방법의 옵션을 제안하였다.

• 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.

• Convergence Security Journal
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• v.22 no.3
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• pp.79-86
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• 2022

Voice phishing is a cyber crime in which fake financial institutions, the Public Prosecutor's Office, and the National Police Agency are impersonated to find out an individual's Certification number and credit card number or withdraw a deposit. Recently, voice phishing has been carried out in a subtle and secret way. Analyzing the trend of voice phishing that occurred in '18~'21, it was found that there is a seasonality that occurs rapidly at a time when the movement of money is intensifying in the trend of voice phishing, giving ambiguity to time series analysis. In this research, we adjusted seasonality using the X-12 seasonality adjustment methodology for accurate prediction of voice phishing occurrence trends, and predicted the occurrence of voice phishing in 2022 using the ARIMA model.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.183-189
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• 2024

In order to effectively predict time series data, this study proposed a hybrid prediction model that decomposes the data into trend, seasonality, and residual components using Seasonal-Trend Decomposition on Loess, and then applies ARIMA to the trend component, Fourier Series Regression to the seasonality component, and XGBoost to the remaining components. In addition, performance comparison experiments including ARIMA, XGBoost, LSTM, EMD-ARIMA, and CEEMDAN-LSTM models were conducted to evaluate the prediction performance of each model. The experimental results show that the proposed hybrid model outperforms the existing single models with the best performance indicator values in MAPE(3.8%), MAAPE(3.5%), and RMSE(0.35) metrics.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1410-1414
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• 2010

최근 들어 가뭄과 국지성 호우 등의 기상이변이 지속적으로 발생하고 있으며, 이는 국민 삶의 발전과 향상에 밀접한 관계가 있는 것으로 전세계적으로 이에 대한 관심이 증가하고 있는 추세이다. 특히 댐의 효율적 관리와 안정적인 운영은 홍수피해 방지, 안정적인 용수공급과 같은 국민 생활과 밀접한 관계를 가지고 있어 수자원의 효율적인 운영과 이용은 장기적인 관점을 통하여 수립해야 한다. 이와 같이 댐 유입량의 예측은 유출모형의 목적 중 중요한 부분으로 확정론적 모형이 시 혹은 일유량과 같은 매우 짧은 시간의 유출을 예측하는데 주로 사용되지만 이는 매개변수의 추정이 불가능하거나 실제유역에서의 측정이 불가능 할 경우에는 모형적용에 한계가 있다. 이에 반해 추계학적 모형에 의한 유출예측은 장기간의 유출을 과거자료의 통계학적 특성변수를 매개변수로 하여 예측하는 방법으로 모형의 적용에 필요한 매개변수가 적어 그 적용성이 간편한 장점이 있다. 본 연구에서는 계절형 ARIMA모형을 적용하여 과거자료의 적용범위, 매개변수의 산정, 적합성 판정에 대하여 판단하고, 이 모형이 월유입량의 예측에 적합한지를 검토하였다.

• 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 Korea Port Economic Association
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• v.23 no.1
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• pp.149-162
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• 2007

The purpose of this study is to forecast the marine trading volumes using multiplicative seasonal Autoregressive Integrated Moving Average(ARIMA) model. The paper proceeds by comparing the forecasting performances of the unload volumes with those of the load volumes with Box-Jenkins ARIMA model. Also, I present the predicted values based on the ARIMA model. The result shows that the trading volumes increase very slowly.

• Journal of the Korean Society for Railway
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• v.17 no.4
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• pp.307-312
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• 2014

This study suggested the ARIMA model taking into consideration the seasonal characteristic factor as a method for efficiently forecasting passenger transport demand of the Joongang Line. The forecasting model was built including the demand for the central inland region tourist train (O-train, V-train), which was opened to traffic in April-, 2013 and run in order to reflect the recent demand for the tourism industry. By using the monthly time series data (103) from January-, 2005 to July-, 2013, the optimum model was selected. The forecasting results of passenger transport demand of the Joongang Line showed continuous increase. The developed model forecasts the short-term demand of the Joongang Line.