• Korea Trade Review
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• v.47 no.6
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• pp.291-305
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• 2022

In this study, the volume of Pohang Port was predicted. All cargo of Pohang port, iron ore, steel, and bituminous coals were selected as prediction targets. SARIMA, Prophet, and Neural Prophet were used as analysis methods. The predictive power of each model was verified, and a predictive model with high performance was used to predict the volume of goods in Pohang port. As a result of the analysis, it was found that Neural Prophet showed the highest performance in all predictive power. As a result of predicting the future volume of goods until August 2027 using Neural Prophet, it was found that the volume of all items in Pohang port was decreasing. In particular, it was analyzed that the decline in steel cargo was steep. In order to increase the volume of cargo at Pohang port, it is necessary to diversify the cargo handled at Pohang port and check the policy of increasing the volume of cargo.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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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.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1037-1046
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• 2008

Recently, the oil suppliers who supply the tax-exempt oil to the fishery are confronted with big trouble in their supply and demand system due to the unstable global oil prices. We applied the seasonal ARIMA(SARIMA) model to the low-sulfur and high-sulfur crude oil which are in great request and developed forecasting systems for them. Since there are many parameters in SARIMA, it is difficult to estimate the optimal parameters, but it is overcome by using simulation looping program. In conclusion, we found that the obvious seasonality in demand of low-sulfur and these demands are tending downwards gradually.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.641-644
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• 2015

우리나라는 지난 40년간 급속한 경제 성장의 과정에서 에너지 소비가 급증하고 있으며, 이로 인해 온실가스 배출량은 1990년~2005년 사이 두 배 이상 증가하였고, 이는 OECD 국가 중 가장 높은 증가율이다. 2차 오염물질인 오존은 1990년부터 2012년까지 연평균 3% 상승하고 있으며, 반복 노출 시 폐에 피해를 줄 수 있는 오염 물질로 예방 대책이 필요하다. 이를 위해 본 논문에서는 계절성 특성을 지닌 오존농도 시계열 데이터를 바탕으로 SARIMA 모형을 활용하여 예측 모형을 구축 하였다.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.53-62
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• 2020

The maritime and port logistics industry is closely related to global trade and economic activity, especially for Korea, which is highly dependent on trade. As the largest port in Korea, Busan Port processes 75% of the country's container cargo; the port is therefore extremely important in terms of the country's national competitiveness. Port container cargo volume forecasts influence port development and operation strategies, and therefore require a high level of accuracy. However, due to unexpected and sudden changes in the port and maritime transportation industry, it is difficult to increase the accuracy of container volume forecasting using existing time series models. Among deep learning models, this study uses the LSTM model to enhance the accuracy of container cargo volume forecasting for Busan Port. To evaluate the model's performance, the forecasting accuracies of the SARIMA and LSTM models are compared. The findings reveal that the forecasting accuracy of the LSTM model is higher than that of the SARIMA model, confirming that the forecasted figures fully reflect the actual measurement figures.

• Economic and Environmental Geology
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• v.50 no.4
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• pp.267-276
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• 2017

Seawater intrusion into coastal fractured rock aquifer, resulting in groundwater contamination, is of serious concern in coastal areas of Jeolla Namdo, Korea, which heavily depends on groundwater resources. Time series analysis and forecasting were carried out to analyze and predict EC which is a major indicator of seawater intrusion. Two time series models of autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) were tested for suggesting appropriate time series model. Time series data of EC measured over one year showed a increasing trend with short periodic fluctuations, due to tidal effect and pumping, which indicated that EC time series data tended to be non-stationary. SARIMA model was found better fitted to observed EC than any other time series model. Time series analysis and modeling was found to be a useful tool to analyze EC at coastal fractured rock aquifer subject to seawater intrusion.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.61-74
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• 2019

In this study, the SPI (Standardized Precipitation Index) of meteorological drought and SDI (Streamflow Drought Index) of hydrological drought for 1, 3, 6, 9, and 12 months duration were estimated to analyse the characteristics of drought using rainfall and dam inflow data for Chungju dam ($6,661.8km^2$) with 31 years (1986-2016) and Boryeong dam ($163.6km^2$) watershed with 19 years (1998-2016) respectively. Using the estimated SPI and SDI, the drought forecasting was conducted using seasonal autoregressive integrated moving average (SARIMA) model for the 5 durations. For 2016 drought, the SARIMA had a good results for 3 and 6 months. For the 3 months SARIMA forecasting of SPI and SDI, the correlation coefficient of SPI3, SPI6, SPI12, SDI1, and SDI6 at Chungju Dam showed 0.960, 0.990, 0.999, 0.868, and 0.846, respectively. Also, for same duration forecasting of SPI and SDI at Boryeong Dam, the correlation coefficient of SPI3, SPI6, SDI3, SDI6, and SDI12 showed 0.999, 0.994, 0.999, 0.880, and 0.992, respectively. The SARIMA model showed the possibility to provide the future short-term SPI meteorological drought and the resulting SDI hydrological drought.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.59-66
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• 2020

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.123-130
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• 2022

In this paper is one of the measures to prevent concrete collapse accidents at construction sites in advance. Analyzed based on accumulated Meteorological Agency data. It is a reliable model that confirms the prediction of the decrease rate occurrence interval, and the verification items such as p_value is 0.5 or less and ecof appears in one direction through the SARIMA model, which is suitable for regular and clear time series data models, ensure reliability. Significant results were obtained. As a result of analyzing the temperature change by time zone and the water reduce ratio by section using the data secured based on such trust, the water reduce ratio is the highest in the 29-31 ℃ section from 12:00 to 13:00 from July to August. found to show. If a factor in the research result interval occurs using the research results, it is expected that the batch plant will produce Ready-mixed concrete that reflects the water reduce ratio at the time of designing the water-cement mixture, and prevent the decrease in concrete compressive strength due to the water reduce ratio.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.768-771
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• 2021

시계열 분석은 이전 시점들의 데이터를 기반으로 미래 시점의 데이터를 예측하는 기술을 제공하며, SARIMA는 이러한 시계열 분석에서 활용되는 통계 모델의 일종이다. 본 연구는 직접 수집한 실시간 위치 데이터에 SARIMA를 적용하여 개인의 이동 패턴을 추출하고 이를 예측에 활용하는 전반적인 프로세스를 제작하였다. 첫째, DB에 업로드된 위치 데이터를 비지도 학습의 일종인 EM-clustering을 활용해 핵심 방문 장소들로부터의 거리에 따라 군집화했다. 둘째, 해당 장소에 입장하고 퇴장하는 시간 간격에 SARIMA를 적용해 주기성을 추출했다. 마지막으로, 이 주기성들을 군집의 중요도에 따라 순차적으로 분석하여 유의미한 예측 결과를 도출해냈다.