• Journal of the Korean Society for Railway
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• v.14 no.5
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• pp.470-476
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• 2011

This study proposed the intervention ARIMA model as a way to forecast the KTX passenger demand. The second phase of the Gyeongbu high-speed rail project and the financial crisis in 2008 were analyzed in order to determine the effect of time series on the opening of a new line and economic impact. As a result, the financial crisis showed that there is no statistically significant impact, but the second phase of the Gyeongbu high-speed rail project showed that the weekday trips increased about 17,000 trips/day and the weekend trips increased about 26,000 trips/day. This study is meaningful in that the intervention explained the phenomena affecting the time series of KTX trip and analyzed the impact on intervention of time series quantitatively. The developed model can be used to forecast the outline of the overall KTX demand and to validate the KTX O/D forecasting demand.

• 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.

• Journal of Navigation and Port Research
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• v.32 no.6
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• pp.425-431
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• 2008

The future cargo transportation and traffic volume on container in Gwangyang port was forecasted by using univariate time series models in this research. And the container ship traffic was produced. The constructed models all were most adapted to Winters' additive models with a trend and seasonal change. The cargo transportation on container in Gwangyang port was estimated each about 2,756 thousand TEU and 4,470 thousand TEU in 2011 and 2015 by increasing each 7.4%, 16.2% compared with 2007. The volume per ship on container was estimated each about 675TEU and 801TEU in 2011 and 2015 by increasing each 30.3%, 54.6% compared with 2007. Also, traffic volume on container incoming in Gwangyang Port was prospected each about 4,078ships and 5,921ships in 2011 and 2015.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.65-73
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• 2001

Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.491-503
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• 1999

시계열자료에는 흔히 대부분의 자료에서 벗어나는 이상치들이 포함되어 있는데, 이러한 자료는 관측치들 사이의 종속구조로 인해 분석과정에 영향을 끼칠 수 있고, 특히 연속시점에서 발생하는 경우에 그 영향이 매우 심각할 수 있다. 본 논문에서는 연속이상치(PO)가 Winters 계절지수모형의 분석과정에 미치는 영향을 유도하고, 예측 평균제곱오차(MSFE)를 구하여 연속이상치가 예측에 미치는 영향을 제시하였다. 또한, 실제자료를 이용하여 연속이상치의 영향을 실증적으로 분석하였다.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.189-204
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• 2018

This study is on forecasting the electricity consumption of an industrial manufacturing building called GGM from January 2014 to April 2017. We fitted models using SARIMA, SARIMA + GARCH, Holt-Winters method and ARIMA with Fourier transformation. We also forecasted electricity consumption for one month ahead and compared the predicted root mean square error as well as the predicted error rate of each model. The electricity consumption of GGM fluctuates weekly and annually; therefore, SARIMA + GARCH model considering both volatility and seasonality, shows the best fit and prediction.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.139-153
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• 2018

This paper investigates solar power forecasting based on several time series models. First, we consider weather variables that influence forecasting procedures as well as compare forecasting accuracies between time series models such as ARIMAX, Holt-Winters and Artificial Neural Network (ANN) models. The results show that ten models forecasting 24hour data have better performance than single models for 24 hours.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.318-321
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• 2000

이 연구는 정보통신기기 수출량에 관해 하향식(Top-down) 방법에 기초한 예측 모형을 제시한다. 하향식방법은 전체 수출량과 전체를 구성하는 개별 항목간에 계층적 관계를 바탕으로 순차적으로 예측을 수행하는 방법이다. 전체와 개별 항목간에 관계는 데이터의 시계열 특성과 데이터에 영향을 주는 요인들에 의해서 만들어진다. 이러한 관계를 바탕으로 하는 하향식 예측은 전체 수출량을 먼저 예측한 후 이 예측치를 바탕으로 하여 개별 항목에 대한 예측을 수행한다. 하지만 하향식 방법은 가장 아래 계층의 예측치를 산출하기 위해 필요한 것이며 최종 예측치는 가장 마지막 계층에서부터 예측 데이터를 합산해서 얻을 수 있다. 결국 하향식 예측 방법은 전체와 개별 항목 사이에 상관관계가 높고 계층화되어 있는 구조에 적합하다. 이 예측 대상이 되는 정보통신기기 수출량에 대한 적용 사례를 살펴보자. 계층 구조를 보면 정보통신기기 전체 수출량과 전체를 구성하는 개별 항목으로 정보통신기기 분류별(유선기기, 무선기기, 방송기기, 정보기기, 기타부품기기)과 국가별(미국, 일본, 중국 등 7 개국)로 나뉘어진다. 다시 이 아래 계층으로는 국가와 정보통신기기의 행렬 구조(예: 미국-유선, 일본-부품 등)에 의해 35 개로 나뉘어진다. 각 단계별 예측 방법을 보면 전체 수출량은 시계열 특성과 거시적 변수를 반영한 시계열 모형, 그 아래 계층인 국가별과 분류별 모형에는 전체 수출량 시계열 특성과 국가별과 분류별에 영향을 주는 관련 변수를 반영한 회귀모형 그리고 행렬 구조에 대한 예측은 상위 계층의 시계열 특성과 행렬구조 데이터의 계절성이 반영된 다중 회귀모형을 이용하였다.ndex, mobile user′s will first be classified by their traffic volume, and then calculate the average tariffs per minute of each group of users, and lastly weight-average those tariffs per minute. And finally, this paper shows the mobile tariff index by considering those averaged tariffs and the carriers′ market shares to reflect the contribution of individual carriers and the users′ traffic volume.완화될 수 있다. 즉, 봉지를 씌웅으로서 봉지 내의 대기 환경이 외기보다 안정적으로 유지되고 직사광선이나 농약 및 마찰로부터 과실을 보호해 주기에 동녹이 어느 정도 방지될 수 있는 것이다. 그러나 기존의 황금배봉지는 동녹의 정도를 완화시킬 뿐 완전히 방지할 수 없었으며, 봉지를 적 용한 재배조건에서의 동녹발생 기구를 정확히 이해하지 못했었기에 효과적으로 봉지의 기능 을 개선하는 것이 불가능하였다. 과설의 미려도는 과실의 맛과 함께 그 가치를 결정짓는 중요한 물성으로서 우리나라 황 금배 재배환경과 특성에 알맞은 배봉지의 제작이 선결될 때, 배 품질의 향상, 안정된 공급이 가능하게 될 것이며 아울러 농가의 수업증대와 수출 경쟁력 강화가 이루어질 수 있을 것으로 판단된다. 이러한 측면에서 황금배 재배농가가 당면한 동녹발생의 문제점을 신속한 해결 을

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.159-167
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• 2000

The purpose of this study is to estimate the stochastic monthly runoff model for the Kunwi south station of Wi-stream basin in Nakdong river system. This model was based on the theory of Box-Jenkins multiplicative ARlMA and the state-space model to simulate changes of monthly runoff. The forecasting monthly runoff from the pair of estimated effective rainfall and observed value of runoff in the uniform interval was given less standard error then the analysis only by runoff, so this study was more rational forecasting by the use of effective rainfall and runoff. This paper analyzed the records of monthly runoff and effective rainfall, and applied the multiplicative ARlMA model and state-space model. For the P value of V AR(P) model to establish state-space theory, it used Ale value by lag time and VARMA model were established that it was findings to the constituent unit of state-space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model.