• Korean Journal of Fisheries and Aquatic Sciences
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• v.29 no.2
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• pp.143-149
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• 1996

Forecasts of the monthly catches of anchovy in Korea were carried out by the seasonal Autoregressive Integrated Moving Average (ARIMA) model and spectral analysis. The seasonal ARIMA model is as follows: $$(1-0.431B)(1-B^{12})Z_t=(1-0.882B^{12})e_t$$ where: $Z_t=value$ at month $t;\;B^{p}$ is a backward shift operator, that is, $B^pZ_t=Z_{t-p};$ and $e_t=error$ term at month t, which is to forecast 24 months ahead the anchovy catches in Korea. The prediction error by the Box-Cox transformation on monthly anchovy catches in Korea was less than that by the logarithmic transformation. The equation of the Box-Cox transformation was $Y'=(Y^{0.58}-1)/0.58$. Forecasts of the monthly anchovy catches for $1991\~1992$, which were compared with the actual catches, had an absolute percentage error (APE) range of $1.0\~63.2\%$. Total observed annual catches in 1991 and 1992 were 170,293 M/T and 168,234 M/T respectively, while the predicted catches were 148,201 M/T and 148,834 M/T $(API\;13.0\%\;and\;11.5\%,\;respectively)$. The spectrum analysis of the monthly catches of anchovy showed some dominant fluctuations in the periods of 2.2, 6.1, 10.2 12.0 and 14.7 months. The spectrum analysis was also useful for selecting the ARIMA model.

• Journal of Navigation and Port Research
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• v.32 no.1
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• pp.81-88
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• 2008

The forecast of a container traffic has been very important for port plan and development. Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate that effectiveness can differ according to the characteristics of ports.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.259-260
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• 2007

The forecast of a container traffic has been very important for port plan and development Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest tint ANNs am be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate tint effectiveness can differ according to the ch1racteristics of ports.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.265-272
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• 2019

In this study, we conducted a prediction study to qualitatively identify the continuous growth rate that causes problems every year for deposit bank mortgage loans, identify the characteristic factors that could once again stabilize, and come up with measures for future quantitative analysis of mortgage loans and growth trends. Based on data analysis using the R program, which is widely used for big data analysis, the parameters of ARIMA model (0.1,1)(0.1,1)[12] were found to be most suitable. In these indicators, estimates over the next five years (60 months) increased 4.5% on average. However, this has limitations that do not reflect socio-environmental factors, which require further study of these limitations.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1395-1399
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• 2010

최근 들어 전 세계적으로 태풍과 가뭄 그리고 국지적인 호우 등의 기상변화로 인하여 수자원 종합적인 개발과 이용계획에 대한 전문적인 예측이 필요하다. 우리나라는 홍수기에 집중적인 강우 발생으로 인하여 평수기와 유입량 차이가 심한 수문특성을 가지고 있어 안정적인 수자원 공급에 대한 장기적인 관점에서 이수와 치수정책을 수립해야 한다. 본 연구는 1985년 1월부터 2008년 12월까지 24년에 해당하는 한정된 기간의 짧은 유출량 자료를 갖는 대청댐 유역에서의 시계열 유입량 특성을 Box-Jenkins모형 또는 ARIMA모형을 적용하여 추계학적 분석을 실시하였다. 월유입량과 같은 비정상성 시계열에 적용될 수 있는 적절한 추계학적 모형을 찾기 위하여 모형의 식별과 모형의 추정, 모형의 검진 등의 3단계에 걸친 분석을 실시하였다. 연구결과 대청댐 월유입량 예측모형으로 승법계절 ARIMA$(0,1,2){\times}(1,1,0)_{12}$이 유도되었으며, 이 모형으로 1, 3, 6, 12개월의 선행기간에 대한 실시간 유입량을 예측하였다. 예측된 유입량을 2008년 실측유입량과 비교한 결과 6개월에 대한 예측의 정확성이 가장 높게 나타났다. 또한 평수기와 홍수기를 구분한 예측도 실시하였으며, 평수기는 1개월 홍수기는 3개월 간격으로 예측하는 것이 가장 적절한 것으로 분석되었다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.14-15
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• 2014

The modular construction is as yet early stage of market in Korea. So It is have difficulty of market demand forecast of the modular building. Therefore, this study was done analysis for market trends of the modular building using ARIMA(Auto Regressive Integrated Moving Average) model by time series data.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.1-14
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• 2013

ARIMA and ARIMA+IGARCH models are fitted and compared for daily Korean won/US dollar exchange rate data over 17 years. A linear structural change model and an autoregressive structural change model are fitted for multiple change-point estimation since there seems to be structural change with this data.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.131-136
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• 2014

In this paper, we develop a fashion products demand forecasting algorithm using ARIMA model and Wavelet Neural Networks model. To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "H" company during 2008-2012 and then performed the proposed method through various analyses. As noted in experimental results, the performance of three types model such as ARIMA, Wavelet Neural Networks and ARIMA + Wavelet Neural Networks show 5.179%, 4.553%, and 4.448.% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict fashion products demand for efficient of operation.