• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.597-602
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• 2004

This study examined the forecasting of instant messinger's users choice using neural network. We used the statistical methods which were Logistic Regression, MDA(Multiple Discriminant Analysis), and ANN(Artificial Neural Network). In the result, the forecasting performance of the ANN was better than conventional model(Logistic Regression, MDA).

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.55-61
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• 1986

Mathematical forecasting models and a practical computer based forecasting system are developed for planning production in a manufacturing and distribution network. The forecasting system works at the highest level of a hierarchical computer-based decision support system consisting of the forecasting system, an aggregate planning system and a shop floor scheduling system. The dynamics of business operations for an actual company have been considered to make this study a unique comprehensive analysis of a real world forecasting problem.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.109-114
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• 2020

The aim of this study is to develop basic artificial neural network models in forecasting the in-sample gross domestic product (GDP) of Malaysia. GDP is one of the main indicators in presenting the macro economic condition of a country as set by the world authority bodies such as the World Bank. Hence, this study uses an artificial neural network-based approach to make predictions concerning the economic growth of Malaysia. This method has been proposed due to its ability to overcome multicollinearity among variables, as well as the ability to cope with non-linear problems in Malaysia's growth data. The selected inputs and outputs are based on the previous literatures as well as the economic growth theory. Therefore, the selected inputs are exports, imports, private consumption, government expenditure, consumer price index (CPI), inflation rate, foreign direct investment (FDI) and money supply, which includes M1 and M2. Whilst, the output is real gross domestic product growth rate. The results of this study showed that the neural network method gives the smallest value of mean error which is 0.81 percent with a total difference of 0.70 percent. This implies that the neural network model is appropriate and is a relevant method in forecasting the economic growth of Malaysia.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.221-229
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• 2021

This study aims to forecast the exchange rate of the Chinese Yuan against the US Dollar by a combination of different models as proposed by Poon and Granger (2003) during the Covid-19 pandemic. For this purpose, we include three uni-variate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Chinese Yuan against the US dollar by two combination criteria i.e. var-cor and equal weightage. After finding out the individual accuracy, the models are then assessed through equal weightage and var-cor methods. Our results suggest that Naïve outperforms all individual & combination of time series models. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models except the Naïve model, with the lowest MAPE value of 0764. The results suggesting that the Chinese Yuan exchange rate against the US Dollar is dependent upon the recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting which commensurate with the literature.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.121-128
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• 2024

Demand forecasting is a critical element of revenue management in the tourism industry. Since the 2010s, with the globalization of the tourism industry and the increase of different forms of marketing and information sharing, such as SNS, forecasting has become difficult due to non-linear activities and unstructured information. Various forecasting models for resolving the problems have been studied, and ML models have been used effectively. In this study, we applied the feature selection technique (NSGA3) to time series models and compared their performance. In hotel demand forecasting, it was found that the TCN model has a high forecasting performance of MAPE 9.73% with a performance improvement of 7.05% compared to no feature selection. The results of this study are expected to be useful for decision support through improved forecasting performance.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.159-175
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• 2002

Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.1
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• pp.17-21
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• 2013

Purpose: Forecasting of petroleum consumption is useful in planning and management of petroleum production and control of air pollution. Research Design, Data and Methodology: ARMA models, sometimes called Box-Jenkins models after the iterative Box-Jenkins methodology usually used to estimate them, are typically applied to auto correlated time series data. Results: Petroleum consumption modeling plays a role key in big urban air pollution planning and management. In this study three models as, MLFF, MLFF with GARCH (1,1) and ARMA(1,1), have been investigated to model the petroleum consumption forecasts. Certain standard statistical parameters were used to evaluate the performance of the models developed in this study. Based upon the results obtained in this study and the consequent comparative analysis, it has been found that the MLFF with GARCH (1,1) have better forecasting results.. Conclusions: Survey of data reveals that deposit of government policies in recent yeas, petroleum consumption rises in Tehran and unfortunately more petroleum use causes to air pollution and bad environmental problems.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.2
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• pp.243-252
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• 2023

Fluctuations in the price of aquaculture products have recently intensified. In particular, wholesale price fluctuations are adversely affecting consumers. Therefore, there is an emerging need for a study on forecasting the wholesale price of aquaculture products. The present study forecasted the wholesale price of olive flounder Paralichthys olivaceus, a representative farmed fish species in Korea, by constructing multivariate long-short term memory (LSTM) and gated recurrent unit (GRU) models. These deep learning models have recently been proven to be effective for forecasting in various fields. A total of 191 monthly data obtained for 17 variables were used to train and test the models. The results showed that the mean average percent error of LSTM and GRU models were 2.19% and 2.68%, respectively.