• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.214-223
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• 2022

The purpose of this study is to present a new industrial land demand prediction method that can consider external economic factors. The analysis model used ARIMA-X, which can consider exogenous variables. Exogenous variables are composed of macroeconomic variable, Business Survey Index, and Composite Economic Index variables to reflect the economic and industrial structure. And, among the exogenous variables, only variables that precede the supply of industrial land are used for prediction. Variables with precedence in the supply of industrial land were found to be import, private and government consumption expenditure, total capital formation, economic sentiment index, producer's shipment index, machinery for domestic demand and composite leading index. As a result of estimating the ARIMA-X model using these variables, the ARIMA-X(1,1,0) model including only the import was found to be statistically significant. The industrial land demand forecast predicted the industrial land from 2021 to 2030 by reflecting the scenario of change in import. As a result, the future demand for industrial land was predicted to increase by 1.91% annually to 1,030.79 km². As a result of comparing these results with the existing exponential smoothing method, the results of this study were found to be more suitable than the existing models. It is expected to b available as a new industrial land forecasting model.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.1-17
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• 2008

In this paper the cargo transportation were forecasted for the North Port in Busan through time series models. The cargo transportation were classified into three large groups; container, oil, general cargo. The seasonal indexes of existing cargo transportation were firstly calculated, and optimum models were chosen among exponential smoothing models and ARIMA models. The monthly cargo transportation were forecasted with applying the seasonal index in annual cargo transportation expected from the models. Thus, the cargo transportation in 2011 and 2015 were forecasted about 22,900 myriad ton and 24,654 myriad ton respectively. It was estimated that container cargo volume would play the role of locomotive in the increase of the future cargo transportation. On the other hand, the oil and general cargo have little influence upon it.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.189-204
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• 2018

This study is on forecasting the electricity consumption of an industrial manufacturing building called GGM from January 2014 to April 2017. We fitted models using SARIMA, SARIMA + GARCH, Holt-Winters method and ARIMA with Fourier transformation. We also forecasted electricity consumption for one month ahead and compared the predicted root mean square error as well as the predicted error rate of each model. The electricity consumption of GGM fluctuates weekly and annually; therefore, SARIMA + GARCH model considering both volatility and seasonality, shows the best fit and prediction.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1481-1489
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• 2014

For predicting yearly total attendances in Korean probaseball games, ARIMA models have been widely adopted so far. In this paper, we discuss two other ways of ARIMAX and growth curves with an exogenous variable to predict the attendances. By using the exogenous variable, it turns out that the prediction has been improved compared to ARIMA. It is concluded that various statistical methods must be considered for better prediction, and its results can be applied to predict the attendances of other pro sports.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.449-457
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• 2022

The Port of Busan is currently ranked as the seventh largest container port worldwide in terms of cargo throughput. However, port competition in the Far-East region is fierce. The growth rate of container throughput handled by the port of Busan has recently slowed down. In this study, we analyzed how economic conditions and multiple external shocks could influence cargo throughput and identified potential implications for port business. The aim of this study was to build a model to accurately forecast port throughput using the ARIMA model, which could incorporate external socio-economic shocks, and the VEC model considering causal variables having long-term effects on transshipment cargo. Findings of this study suggest that there are three main areas affecting container throughput in the port of Busan, namely the Russia-Ukraine war, the increased competition for transshipment cargo of Chinese ports, and the weaker growth rate of the Korean economy. Based on the forecast, in order for the Port of the Port of Busan to continue to grow as a logistics hub in Northeast-Asia, policy intervention is necessary to diversify the demand for transshipment cargo and maximize benefits of planned infrastructural investments.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.297-309
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• 2024

This paper evaluates the performance of the neural network autoregressive model combined with an exponential smoothing model, called the NNARX+ETS model. The combined model utilizes the components of ETS as exogenous variables for NNARX, to forecast time series data using artificial neural networks. The main idea is to enhance the performance of NNAR using only lags of the original time series data, by combining traditional time series analysis methods with the neural networks through NNARX. We employ two real data for performance evaluation and compare the NNARX+ETS with NNAR and traditional time series analysis methods such as ETS and ARIMA (autoregressive integrated moving average) models.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.295-303
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• 2017

In this paper, we analyze the trends of the international shipping market and the domestic and foreign factors of the crisis of the domestic shipping market, and identify the characteristics of the recovery of the Busan New Port trade volume which has decreased since the crisis of the domestic shipping market We quantitatively analyzed the future volume of Busan New Port and analyzed the trends of the prediction and recovery trends. As a result of analyzing Busan New Port container cargo volume by using big data analysis tool R, the variation of Busan New Cargo container cargo volume was estimated by ARIMA model (1,0,1) (1,0,1)[12] Estimation error, AICc and BIC were the most optimal ARIMA models. Therefore, we estimated the estimated value of Busan New Port trade for 36 months by using ARIMA (1, 0, 1)[12], which is the optimal model of Busan New Port trade, and estimated 13,157,184 TEU, 13,418,123 TEU, 13,539,884 TEU, and 4,526,406 TEU, respectively, indicating that it increased by about 2%, 2%, and 1%.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.187-194
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• 2019

Traditional demand forecasting methods are difficult to meet the needs of companies due to rapid changes in the market and the diversification of individual consumer needs. In a diversified production environment, the right demand forecast is an important factor for smooth yield management. Many of the existing predictive models commonly used in industry today are limited in function by little. The proposed model is designed to overcome these limitations, taking into account the part where each model performs better individually. In this paper, variables are extracted through Gray Relational analysis suitable for dynamic process analysis, and statistically predicted data is generated that includes characteristics of historical demand data produced through ARIMA forecasts. In combination with the LSTM model, demand forecasts can then be calculated by reflecting the many factors that affect demand forecast through an architecture that is structured to avoid the long-term dependency problems that the neural network model has.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.387-387
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• 1999

The traditional holiday, Sol which is based on a lunar calendar, falls in January orFebruary and makes it difficult to analyze time series data accurately. To analyze whetherSol raises consumer prices or not, RegARIMA models and paired t tests are used. It isfound that Sol raises consumer prices of food products significantly, but So1's effects onconsumer prices of all items are not significant.