• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.59-64
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• 2020

In the Paris Climate Agreement, Korea submitted an ambitious goal of reducing the greenhouse gas emission forecast (BAU) by 37% by 2030. And as one of the countermeasures, a smart grid, an intelligent power grid, was presented. In order to apply the smart grid, EMS(Energy Management System) needs to be installed and operated in various fields, and the supply is delayed due to the lack of awareness of users and the limitations of system ROI. Therefore, recently, various data analysis and control technologies have been proposed to increase the efficiency of the installed EMS. In this study, we present a measurement control algorithm that analyzes and predicts big data collected by IoT using a SARIMA model to check and operate energy consumption of public sports facilities.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.87-95
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• 2020

The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.4
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• pp.443-463
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• 2024

This study sought to improve the accuracy of precipitation prediction by utilizing monthly precipitation data for each region over the past 30 years. Using statistical models (ARIMA, SARIMA) and deep learning models (LSTM, GBM), we learned monthly precipitation data from 1983 to 2012 in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. Based on this, monthly precipitation was predicted for 10 years from 2013 to 2022. As a result of the prediction, most models accurately predicted the precipitation trend, but showed a tendency to underpredict the actual precipitation. To solve these problems, appropriate models were selected for each region and season. The LSTM model showed suitable results in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. When comparing forecasting power by season, the SARIMA model showed particularly suitable forecasting performance in winter in Gangneung, Gwangju, Daegu, Daejeon, Seoul, and Chuncheon. Additionally, the LSTM model showed higher performance than other models in the summer when precipitation is concentrated. In conclusion, closely analyzing regional and seasonal precipitation patterns and selecting the optimal prediction model based on this plays a critical role in increasing the accuracy of precipitation prediction.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.507-516
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• 2012

We study time series models for seasonal time series data with a covariance structure that depends on time and the periodic autocorrelation at various lags $k$. In this paper, we introduce an ARMA model with periodically varying coefficients(PARMA) and analyze Arosa ozone data with a periodic correlation in the practical case study. Finally, we use a PARMA model and a seasonal ARIMA model for data analysis and show the performance of a PARMA model with a comparison to the SARIMA model.

• Environmental Engineering Research
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• v.22 no.2
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• Culinary science and hospitality research
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• v.15 no.2
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• pp.231-242
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• 2009

One of the most crucial information at the hotel banquet is revenue data. Revenue forecast enables cost reduction, increases staffing efficiency, and provides information that helps maximizing competitive advantages in unforeseen environment. This research forecasts the hotel banquet revenue by utilizing ARIMA Model which was assessed as the appropriate forecast model for international researches. The data used for this research was based on the monthly banquet revenue data of G hotel at Seoul. The analysis results showed that SARIMA(2, 1, 3)(0, 1, 1) was finally presumed. This research implied that the ARIMA model, which was assessed as the appropriate forecast model, was applied for analyzing the monthly hotel banquet revenue data. Additionally, the research provides beneficial information with which hotel banquet professionals can utilize as a reference.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.83-91
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• 2021

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

• Journal of Korean Society of Rural Planning
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• v.17 no.1
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• pp.1-11
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• 2011

본 연구에서는 ARIMA(Autoregressive Integrated Moving Average) 모델을 이용하여 농촌관광마을의 월별 관광객을 추정하였다. 단일 마을에 대한 시계열 자료를 경상북도 안동시에 위치한 하회마을을 대상으로 구축하였다. 월별 시계열 자료는 2000년부터 2010년까지 구성되었는데(2008년도 누락), 2000년에서 2007년까지 자료는 최적 모델의 도출에 나머지는 예측치의 검정에 사용되었다. 연구 결과 최적모델에 필요한 시계열 자료의 길이는 6년으로 나타났으며, 최적모델은 계절성을 고려한 SARIMA(2,1,1)(1,1,2)12로 나타났다. 최적 시계열 년수로 나타난 6년을 사용하여 2000-2005, 2001-2006, 그리고 2002-2007의 자료로부터 각각 SARIMA(2,1,1)(1,1,2)12를 도출하여, 차기년도들에 대한 예측결과를 비교한 결과, 높은 $R^2$값을 보였다.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.472-479
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• 2024

Half of the world's total population lives in cities, continuous urbanization is progressing, and the urban population is expected to exceed two-thirds of the total population by 2050. To resolve this phenomenon, the Korean government is focusing on building a new urban air mobility (UAM) industrial ecosystem. Airlines are also part of the UAM industry ecosystem and are preparing to improve efficiency in safe operations, passenger safety, aircraft operation efficiency, and punctuality. This study performs demand forecasting using time series data on the number of daily passengers on Korean Air's Gimpo to Jeju route from 2019 to 2023. For this purpose, statistical and machine learning models such as SARIMA, Prophet, CatBoost, and Random Forest are applied. Methods for effectively capturing passenger demand patterns were evaluated through various models, and the machine learning-based Random Forest model showed the best prediction results. The research results will present an optimal model for accurate demand forecasting in the aviation industry and provide basic information needed for operational planning and resource allocation.