• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Management & Information Systems Review
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• v.35 no.3
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• pp.1-22
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• 2016

We analyzed the international comovements and structural changes in the quarterly real GDP by the Markov-switching vector autoregressive model (MS-VAR) from 1971(1) to 2016(1). The main results of this study were as follows. First, the business cycle phenomenon that occurs in the models or individual time series in real GDP has been grasped through the MS-VAR models. Unlike previous studies, this study showed the significant comovements, asymmetry and structural changes in the MS-VAR model using a real GDP across countries. Second, even if there was a partial difference, there were remarkable structural changes in the economy contraction regime(recession), such as 1988(2) ending the global oil shock crisis and 2007(3) starting the global financial crisis by the MS-VAR model. Third, large-scale structural changes were generated in the economic expansion and/or contraction regime simultaneously among countries. We found that the second world oil shocks that occurred after the first global oil shocks of 1973 and 1974 were the main reasons that caused the large-scale comovements of the international real GDP among countries. In addition, the spillover between Korea and 5 countries has been weak during the Asian currency crisis from 1997 to 1999, but there was strong transmission between Korea and 5 countries at the end of 2007 including the period of the global financial crisis. Fourth, it showed characteristics that simultaneous correlation appeared to be high due to the country-specific shocks generated for each country with the regime switching using real GDP since 1973. Thus, we confirmed that conclusions were consistent with a number of theoretical and empirical evidence available, and the macro-economic changes were mainly caused by the global shocks for the past 30 years. This study found that the global business cycles were due to large-scale asymmetric shocks in addition to the general changes, and then showed the main international comovements and/or structural changes through country-specific shocks.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.73-89
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• 2021

The handysize bulk carriers are capable of transporting a variety of cargo that cannot be transported by mid-large size ship, and the spot chartering market is active, and it is a market that is independent of mid-large size market, and is more risky due to market conditions and charterage variability. In this study, Granger causality test, the Impulse Response Function(IRF) and Forecast Error Variance Decomposition(FEVD) were performed using monthly time series data. As a result of Granger causality test, coal price for coke making, Japan steel plate commodity price, hot rolled steel sheet price, fleet volume and bunker price have causality to Baltic Handysize Index(BHSI) and charterage. After confirming the appropriate lag and stability of the Vector Autoregressive model(VAR), IRF and FEVD were analyzed. As a result of IRF, the three variables of coal price for coke making, hot rolled steel sheet price and bunker price were found to have significant at both upper and lower limit of the confidence interval. Among them, the impulse of hot rolled steel sheet price was found to have the most significant effect. As a result of FEVD, the explanatory power that affects BHSI and charterage is the same in the order of hot rolled steel sheet price, coal price for coke making, bunker price, Japan steel plate price, and fleet volume. It was found that it gradually increased, affecting BHSI by 30% and charterage by 26%. In order to differentiate from previous studies and to find out the effect of short term lag, analysis was performed using monthly price data of major cargoes for Handysize bulk carriers, and meaningful results were derived that can predict monthly market conditions. This study can be helpful in predicting the short term market conditions for shipping companies that operate Handysize bulk carriers and concerned parties in the handysize chartering market.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.