• 전력전자학회논문지
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• 제27권4호
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2000년도 학술발표회 발표논문집
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• pp.590-598
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• 2000

A multiplicative ARIMA model was tested and applied to analyze the periodicity and trends of 168 monthly oxygen demand data from the Noryanggin water quality gauging station in the downstream Han River. ARIMA model was identified to fit to the data using ACF and PACF tests, and the parameters estimated using an unconditional least square method. The residuals between the observed and forecasted data were acceptable with the Porte-Manteau test. A forecast of DO changes was made for its applications.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.143-154
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• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• 응용통계연구
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• 제26권1호
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• pp.1-14
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• 2013

미국 달러에 대한 한국원화의 17년간 일별 원/달러 환율 시계열 데이터에 대하여 정상 시계열 ARIMA 모형과 변동성을 포함한 시계열 모형인 ARIMA+IGARCH 모형을 적합하여 비교하고 예측을 실시하였다. 또한 환율 데이터에 구조변화가 있어 보이므로 선형구조를 포함한 구조 변화 모형과 자기상관 구조를 포함한 구조 변화 모형을 이용하여 변화점을 추정하고자 한다.

• 대한교통학회지
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• 제32권6호
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• pp.600-614
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• 2014

본 연구는 SARIMA 모형을 활용하여 기존에 다루어지지 않았던 분기별 항만 컨테이너 물동량을 예측하였다. 구체적으로 모델 추정에 활용된 자료는 1994년 1사분기부터 2010년 4사분기까지 총 84분기동안의 국내 전체 항만 컨테이너 물동량 자료이다. 본 연구에서 추정된 예측 모형의 예측 정확도를 검증하기 위하여 2011년 1사분기부터 2013년 4사분기까지 물동량을 예측하여 실제 물동량과 비교하였다. 또한 기존에 널리 활용되고 있는 ARIMA 모형을 활용하여 추정한 예측 모형과의 비교를 통해 분기별 항만 물동량 예측에 있어서 SARIMA 모형의 상대적 우수성을 검증하였다. 기존에 항만 물동량을 예측하는 대부분의 연구는 주로 장기 예측에 초점이 맞추어져 있다. 또한 월별, 연도별 물동량 자료가 활용된 경우가 대부분이다. 분기별 항만 컨테이너 물동량 자료를 활용하여 단기 수요를 예측함과 동시에 SARIMA 모형의 우수성을 입증한 본 연구는 충분한 가치가 있다고 판단된다.

• 유통과학연구
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• 제14권11호
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• pp.83-90
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• 2016

Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

• 농촌지도와개발
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• 제19권2호
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• pp.301-320
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• 2012

The study uses a seasonal ARIMA model to forecast the number of tourists of Yeongdeok in an uni-variable time series. The monthly data for time series were collected ranging from 2006 to 2011 with some variation between on-season and off-season tourists in Yeongdeok county. A total of 72 observations were used for data analysis. The forecast multiplicative seasonal ARIMA(1,0,0)$(0,1,1)_{12}$ model was found the most appropriate one. Results showed that the number of tourists was 10,974 thousands in 2012 and 13,465 thousands in 2013, It was suggested that the grasping forecast model is very important in respect of how experts in tourism development in Yeongdeok county, policy makers or planners would establish strategies to allocate service in Yeongdeok tourist destination and provide tourism facilities efficiently.

• 한국ITS학회 논문지
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• 제19권3호
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• pp.28-37
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• 2020

본 연구는 공공자전거의 대여량을 예측하는 딥러닝 모형을 개발하였다. 이를 위하여 공공자전거 대여량 자료, 기상 자료, 그리고 지하철 이용량 자료를 수집하였다. 지수평활 모형, ARIMA 모형과 LSTM기반의 딥러닝 모형을 구축한 후 MSE와 MAE 평가 지표를 사용하여 예측 오차를 비교·평가하였다. 평가 결과, 지수평활 모형으로 MSE 348.74, MAE 14.15 값이 산출되었다. ARIMA 모형으로 MSE 170.10, MAE 9.30 값을 얻었다. 그리고 딥러닝 모형으로 MSE 120.22, MAE 6.76 값이 산출되었다. 지수평활 모형의 값과 비교하여 ARIMA 모형의 MSE는 51%, MAE는 34% 감소하였다. 그리고 딥러닝 모형의 MSE는 66%, MAE는 52% 감소하여 딥러닝 모형의 오차가 가장 적은 것으로 파악되었다. 이러한 결과로부터 공공자전거 대여량 예측 분야에서 딥러닝 모형의 적용시 예측 오차를 크게 감소시킬 수 있을 것으로 판단된다.

• 응용통계연구
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• 제16권1호
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• pp.71-85
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• 2003

일정 시간간격으로 품질을 측정하는 공정관리절차의 경제적 설계에서는 그 특성의 규명이 측정시점의 이산성 (discreteness) 때문에 복잡하고 어렵다. 이 논문에서는 공정 탐색 절차를 Markov 연쇄(chain)로 표현하는 과정을 개발하였고, 공정분포가 공정주기 내에서 발생하는 잡음과 이상원인의 효과를 설명할 수 있는 ARIMA(0,1,1) 모형을 따를 때에 Markov 연쇄의 표현을 이용하여 공정탐색절차의 특성을 도출하였다. Markov 연쇄의 특성은 전이행렬에 따라 달라지며, 전이행렬은 관리절차와 공정분포에 의해 결정된다. 이 논문에서 도출된 Markov 연쇄의 표현은 많은 다른 형태의 관리절차나 공정분포에서도 그에 해당하는 전이행렬을 구하면 쉽게 적용될 수 있다.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.