• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.4
• /
• pp.59-66
• /
• 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 Korean Society of Agricultural Engineers
• /
• v.61 no.1
• /
• pp.61-74
• /
• 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.

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

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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2004.05b
• /
• pp.1208-1212
• /
• 2004

다목적댐을 효율적이고 체계적으로 운영하기 위해서는 수문순환에 대한 지역별, 기간별 이해와 더불어 댐저수지로의 정확한 유입량 산정이 필요하다. 수문모델링을 비교하기 위해서는 개념적 모형과 추계학적 모형으로 나눌 수 있는데 개념적 모형은 상당히 많은 입력요소로 말미암아 사용자로 하여금 이해를 하는데 있어서 어려움을 겪을 수 밖에 없는 실정이나 추계학적 모형은 확률적 철상 및 기초적 예측이론을 습득하게 되면 쉽고 간단하여 검토를 용이하게 할 수 있는 장점이 있다. 수자원시스템의 설계, 계획, 운영에 있어서 핵심적인 수문변수의 미래거동의 보다 나은 추정치가 필요하다. 예를 들어, 수력발전, 레크리에이션 이용과 하류지역의 오염희석과 같은 다중 목적을 유지하기 위하여 다목적댐을 운영할 때에, 다가오는 미래시간에 대한 계획된 유입량의 예측이 요구된다. 예측의 목적은 미래에 발생한 정확한 예측을 제공하는 것이다. 따라서 월유입량 예측을 위해 추계학적 모형(ARMA(1,1), ARMAX, TFN, SARIMA)과 신경망 모형(BP, CASCADE 등)의 적용을 통해 한강수게 주요 다목적댐에 가장 적합한 방법을 선정하고자 하는데 본 연구의 목적이 있다.

• Journal of Digital Convergence
• /
• v.17 no.9
• /
• pp.135-145
• /
• 2019

Prolonged recession has caused the youth unemployment rate in Korea to remain at a high level of approximately 10% for years. Recently, the number of unemployed Koreans in their 30s and 40s has shown an upward trend. To expand the government's employment promotion and unemployment benefits from youth-centered policies to diverse age groups, including people in their 30s and 40s, prediction models for different age groups are required. Thus, we aimed to develop unemployment prediction models for specific age groups (30s and 40s) using available unemployment rates provided by Statistics Korea and Google search queries related to them. We first estimated multiple linear regressions (Model 1) using seasonal autoregressive integrated moving average approach with relevant unemployment rates. Then, we introduced Google search queries to obtain improved models (Model 2). For both groups, consequently, Model 2 additionally using web queries outperformed Model 1 during training and predictive periods. This result indicates that a web search query is still significant to improve the unemployment predictive models for Koreans. For practical application, this study needs to be furthered but will contribute to obtaining age-wise unemployment predictions.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.5
• /
• pp.857-867
• /
• 2009

The purpose of this paper is to suggest the methods of forecasting for the number of the elementary, middle and high-school student based on the proportion of promotion until 2026 year. The suggested methods are the proportion of promotion, mov baseverage, Holt-W bters model, SARIMA, regression fit. As the result, the abilities of forecasting by the method of moving average are better than those of other methods.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.8A
• /
• pp.1153-1159
• /
• 2000

This paper applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the lido utilization that QoS of the network is greatly influenced by and proposes the prediction algorithm of threshold violation in line utilization using the seasonal ARIMA model. We can predict the time of threshold violation in line utilization and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold and a detection probability, it thus appears that we have maximized the performance of this algorithm.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.32 no.5
• /
• pp.322-330
• /
• 2020

Modeling for outliers in time series was carried out using the MSL and extreme high, low tide levels (EHL, HLL) data set in the Busan and Mokpo stations. The time-series model is seasonal ARIMA model including the components of the AO (additive outliers) and LS (level shift). The optimal model was selected based on the AIC value and the model parameters were estimated using the 'tso' function (in 'tsoutliers' package of R). The main results by the model application, i.e.. outliers and level shift detections, are as follows. (1) The two AO are detected in the Busan monthly EHL data and the AO magnitudes were estimated to 65.5 cm (by typhoon MAEMI) and 29.5 cm (by typhoon SANBA), respectively. (2) The one level shift in 1983 is detected in Mokpo monthly MSL data, and the LS magnitude was estimated to 21.2 cm by the Youngsan River tidal estuary barrier construction. On the other hand, the RMS errors are computed about 1.95 cm (MSL), 5.11 cm (EHL), and 6.50 cm (ELL) in Busan station, and about 2.10 cm (MSL), 11.80 cm (EHL), and 9.14 cm (ELL) in Mokpo station, respectively.