• 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).

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.237-244
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• 2011

In call forecasting literature, both the seasonal autoregressive integrated moving average(ARIMA) type models and seasonal linear models have been popularly suggested as competing models. However, their parallel comparison for the forecasting accuracy was not strictly investigated before. This study evaluates the accuracy of both the seasonal linear models and the seasonal ARIMA-type models when predicting intra-day call arrival rates using both real and simulated data. The seasonal linear models outperform the seasonal ARIMA-type models in both one-day-ahead and one-week-ahead call forecasting in our empirical study.

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.196-209
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• 2015

Retail sales forecast is a special area of forecasting. Its unique characteristics call for unique data models and treatment, and unique forecasting processes. In this paper, we will address lessons learned and challenges encountered in retail sales forecast from a practical and technical perspective. In particular, starting with the data models of retail sales data, we proceed to address issues existing in estimating and processing each component in the data model. We will discuss how to estimate the multi-seasonal cycles in retail sales data, and the limitations of the existing methodologies. In addition, we will talk about the distinction between business events and forecast events, the methodologies used in event detection and event effect estimation, and the difficulties in compound event detection and effect estimation. For each of the issues and challenges, we will present our solution strategy. Some of the solution strategies can be generalized and could be helpful in solving similar forecast problems in different areas.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.83-92
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• 2022

Due to the uncertainty in the order of the integrated model, the SARIMA-LSTM model, SARIMA-SVR model, LSTM-SARIMA model, and SVR-SARIMA model are constructed respectively to determine the best-combined model for forecasting the China-Russia trade turnover. Meanwhile, the effect of the order of the combined models on the prediction results is analyzed. Using indicators such as MAPE and RMSE, we compare and evaluate the predictive effects of different models. The results show that the SARIMA-LSTM model combines the SARIMA model's short-term forecasting advantage with the LSTM model's long-term forecasting advantage, which has the highest forecast accuracy of all models and can accurately predict the trend of China-Russia trade turnover in the post-epidemic period. Furthermore, the SARIMA - LSTM model has a higher forecast accuracy than the LSTM-ARIMA model. Nevertheless, the SARIMA-SVR model's forecast accuracy is lower than the SVR-SARIMA model's. As a result, the combined models' order has no bearing on the predicting outcomes for the China-Russia trade turnover time series.

• Journal of Korean Clinical Health Science
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• v.2 no.2
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• pp.126-132
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• 2014

Purpose. Because traditional statistics approach had limitations in learning future forecasting and major factors causing occupational injuries in each industry, this paper develops a model forecasting and evaluating occupational injury rate by using a system dynamics model through the analysis of the industry injury statistics and the project for industry injury prevention. Method. The model of this paper consists of 12 total models such as a model of employees, of industrial disaster victims, of injury rate, etc.; In the analysis of firm size, it is classified and developed according to 12 groups on the basis of the number of employees, and in the analysis of industrial classification, it is done according to 10 total business fields such as manufacturing business, construction one, etc. Results. This paper suggests the methodology which forecasts industry injury rate by business field and size on the basis of developed model, and evaluates an industry injury prevention project from various angles. Conclusions. This paper deduced problem through the analysis of an industry injury by business fields and a comparative analysis of foreign cases, and analyzed to affect industry injury prevention by industry. And it also analyzed actual condition of industry injury, and did a difference in the level of safety consciousness according to the general characteristics of workers and occupational safety and health education related characteristics. In result, this paper suggests that analyzing occupational injury related factors, a safety budgetary allocation, and industry injury related factors can reduce illness costs such as employees' injury and medical care, and also assist cost for a disability.

• Journal of Korean Society of Rural Planning
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• v.14 no.3
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• pp.1-9
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• 2008

In this study, by reflecting flow of age, possibility of new theme park development as private investments business based on source that is farming village that is not tried to before is verified and by analyzing potential of the site, effectiveness of new theme park development is examined. "Nonghyup Anseong Meadow Anseong-si Gyeonggi-do" is selected as researched site where accessibility is good as there is near to National Capital region and nature condition is also good. Demands are forecasted using visiting intention and realizing index through analogical method and by analyzing existing data related with increase of tourism business that people can experience English village and increasing demand of experiencing farming region tourism demands are forecasted. The results are at below. First, As average expenditure per one person is 52,209 won that is shown in result of survey, if multiplying increasing rate of price and the number of visiting people that is optimistic forecasting figure, the whole expenditure of visitors per one year is from 10.54 billions to 13.85 billions won. Second is potential power of demand aspects. Potential power of that theme park was re-examined through demands forecasting analysis through survey. Experiencing farming regions theme park business that is informed through analysis of potential power of development and demand aspects has value to invest as new business based on farming regions sources, as a result of searching through diverse aspects such as tourism, economy, public interest and cultural aspect and so on.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.473-481
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• 2010

Forecasting for air demand such as international passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison of the performances of the multivariate time series models. In this paper, we used real data such as exchange rates, oil prices and export amounts to predict the future demand on international passenger and freight.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.647-658
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• 2021

Analysts' forecasts are important for providing useful guidance to investors, especially individual or small investors, and therefore it becomes critical to identify the elements which can potentially increase errors in analysts' forecasts. This study investigates potential factors which can lead to errors in forecasting by analysts, specifically in terms of the level and attributes of corporate earnings. Utilizing a sample of firms listed on the Korean stock markets, this study provides evidence that firms with more volatile and unpredictable earnings feature less accurate analyst forecasts. This study fills a void in the literature by conducting empirical tests for earnings attributes in terms of volatility and unpredictability that could potentially undermine the forecast accuracy. The negative association between the quality of earnings and forecast accuracy is more pronounced for firms with negative net income values. Additional analysis demonstrates that forecast accuracy is significantly lower for the fourth quarter than for other fiscal quarters and that fourth quarter earnings tend to be more volatile and unpredictable. This study contributes to the literature by providing new empirical evidence regarding the comprehensive effects of earnings quality and level on analysts' forecasting accuracy and further suggests potential factors contributing to the fourth quarter anomaly in analyst forecasts in terms of earnings attributes.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.31-42
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• 2022

Purpose - The main research goals by macroeconomic analysis is to assess the effectiveness of state regulation, the sustainability of development, and the financial stability of the state. Research design, Data, and methodology - The research were analyzed using the methods of multivariate statistics and application of the software package Stat graphics. The volume of data from the 1995 to the 2021 was analyzed by Russian Federation. The scale of research on Belarus: to be analyzed the amount of data from the 2015 by 2021, on Kazakhstan - from the 19941, on Kyrgyzstan - from the 2002, on Tajikistan - from the 2008, on Armenia - from the 2021, on Japan - since the 1970, on China - since the 1950, on South Korea - since the 1953. Result - The methods of multivariate statistics was demonstrated exact of result in forecasting of macroeconomic indicators. The most of tendency with the accurate results of are described using the second-degree polynomials. In the most research of country there are the macroeconomic proportion are broken. Conclusion - In the countries studied, the monetary aggregates have a significant growth rate. The shares with a substantial monetary stock and the speed of its growth are divided in the two groups: having placements in the real sectors of the economy and not having received the same result of development from the growth of the monetary stock.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.7-14
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• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.