• 응용통계연구
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• 제30권1호
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• pp.181-193
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• 2017

본 논문에서는 계층적 시계열 자료 분석을 위한 대표적인 두 가지 방법인 상향식과 최적조합 예측법을 소개한다. 이러한 예측법은 계층적 시계열을 구성하는 모든 계열을 예측해야 하는 독립적 예측과 달리, 임의의 조정 과정이 없이 하위 계층 계열의 예측값의 합은 항상 상위 계층의 예측값과 일치하게 된다. 또한, 독립적 예측과 비교하여 예측력을 향상시킨다. 계층적 예측법의 효율성을 살펴보기 위하여 국내 16개 시도별 남녀 교통사고 발생건수 시계열 자료를 예측하였다. 이를 통하여 교통사고 발생건수에 대한 각 계층의 예측에서 계층적 방법과 독립적 방법의 차이점 및 우수성을 비교하였다.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.141-147
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• 2006

BOK-X12-ARIMA has been developed by the Bank of Korea in order to accomodate special features such as lunar effect, labor day and election effect which are intrinsic in Korean seasonal time series. Irregular component resulting from BOK-X12-ARIMA is usually treated as white noise time series. If this shows dependency structure, it may be advisable to incorporate dependency in irregular component into prediction. This article illustrates how to refine point forecast using dependency structure in irregular component.

• 한국태양에너지학회 논문집
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• 제33권6호
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.9-17
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• 2013

Purpose: The goal of this study was to develop a methodology for the demand forecast of tractor, riding type rice transplanter and combine harvester using an ARIMA (autoregressive integrated moving average) model, one of time series analysis methods, and to forecast their demands from 2012 to 2021 in South Korea. Methods: To forecast the demands of three kinds of machines, ARIMA models were constructed by following three stages; identification, estimation and diagnose. Time series used were supply and stock of each machine and the analysis tool was SAS 9.2 for Windows XP. Results: Six final models, supply based ones and stock based ones for each machine, were constructed from 32 tentative models identified by examining the ACF (autocorrelation function) plots and the PACF (partial autocorrelation function) plots. All demand series forecasted by the final models showed increasing trends and fluctuations with two-year period. Conclusions: Some forecast results of this study are not applicable immediately due to periodic fluctuation and large variation. However, it can be advanced by incorporating treatment of outliers or combining with another forecast methods.

• 상하수도학회지
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• 제21권6호
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• pp.701-707
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• 2007

The information on the incoming load to wastewater treatment plants is not often available to apply to evaluate effects of control actions on the field plant. In this study, a time series model was developed to forecast influent flow rate, BOD, COD, SS, TN and TP concentrations using field operating data. The developed time series model could predict 1 day ahead forecasting results accurately. The coefficient of determination between measured data and 1 day ahead forecasting results has a range from 0.8898 to 0.9971. So, the corelation is relatively high. We made forecasting program based on the time series model developed and hope that the program will assist the operators in the stable operation in wastewater treatment plants.

• 한국보육지원학회지
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• 제12권6호
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• pp.123-137
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• 2016

The purpose of this study was to forecast demand of childcare teachers based ion four different scenarios. In order to, the demand for childcare teachers from 2015 to 2024 were forecasted using time series techniques with data on the number of childcare teachers from 2003 to 2014. Results were as followings. Firstly, the demand for childcare teachers was expected to increase until 2019, but after 2020 steadily decreased in terms of scenario 1(child teacher ratio regulation). According to scenario 2(child teacher ratio based on 17 cities and provinces), the demand for childcare teachers was expected to need 440 teachers more until 2016. Then, according to scenario 3(two teachers each class), Scenario 4-1(one teacher and one staff each 2 toddler class and 3 older class) and scenario 4-2(one teacher and one staff each class), the demand of childcare teachers and staffs were estimated. These results implicated that childcare teachers and staffs supply policy would be established according to forecast demand.

• 도시과학
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• 제7권1호
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• pp.45-54
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• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• 전기학회논문지
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• 제65권6호
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• pp.918-922
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• 2016

As the number of wind farms operating has increased, the interest of the central unit commitment and dispatch for wind power has increased as well. Wind power forecast is necessary for effective power system management and operation with high wind power penetrations. This paper presents the centralized wind power forecasting method, which is a forecast to combine all wind farms in the area into one, using time series models. Also, this paper proposes a prediction model modified with wind forecast error compensation. To demonstrate the improvement of wind power forecasting accuracy, the proposed method is compared with persistence model and new reference model which are commonly used as reference in wind power forecasting using Jeju Island data. The results of case studies are presented to show the effectiveness of the proposed wind power forecasting method.

• 응용통계연구
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• 제32권6호
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• pp.851-865
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• 2019

하나의 시계열 자료에서 다양한 특징을 발견하는 일은 간단한 문제가 아니다. 본 논문에서는 하나의 시계열 자료에서 복수의 패턴을 찾아내어 예측 정확도를 높이는 방식인 다중 결합 예측 알고리즘을 소개한다. 이 알고리즘은 시간적 결합과 예측값 조합의 개념을 사용한다. 시간적 결합 방식을 통해, 하나의 시계열 자료에서 여러 개의 시계열 자료를 생성할 수 있으며, 각각의 자료는 별도의 특성을 가지게 된다. 여러 개의 시계열 자료에서 다양한 특성을 추출하기 위하여 지수평활법을 사용하고 시계열 요소들 및 이들의 예측값을 계산한다. 마지막 단계에서 시계열 요소 별로 예측값을 혼합 한 후, 각 시계열 요소들의 조합값을 더하여 최종 예측값을 만든다. 실증 분석으로 국내 교통사고 발생 건수를 예측한다. 분석 결과, 기존의 다른 예측 방식보다 예측 성능이 우수함을 확인할 수 있다.

• The Journal of Asian Finance, Economics and Business
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• 제9권10호
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• pp.159-168
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• 2022

The time series of foreign trade turnover is complex and variable and contains linear and nonlinear information. This paper proposes preprocessing the dataset by the EMD algorithm and combining the linear prediction advantage of the SARIMA model with the nonlinear prediction advantage of the EMD-LSTM model to construct the SARIMA-EMD-LSTM hybrid model by the weight assignment method. The forecast performance of the single models is compared with that of the hybrid models by using MAPE and RMSE metrics. Furthermore, it is confirmed that the weight assignment approach can benefit from the hybrid models. The results show that the SARIMA model can capture the fluctuation pattern of the time series, but it cannot effectively predict the sudden drop in foreign trade turnover caused by special reasons and has the lowest accuracy in long-term forecasting. The EMD-LSTM model successfully resolves the hysteresis phenomenon and has the highest forecast accuracy of all models, with a MAPE of 7.4304%. Therefore, it can be effectively used to forecast the Sino-Russia foreign trade turnover time series post-epidemic. Hybrid models cannot take advantage of SARIMA linear and LSTM nonlinear forecasting, so weight assignment is not the best method to construct hybrid models.