• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.91-103
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• 2019

An accurate prediction on near-future cash flows plays an important role for a company to attenuate the shortage risk of cash flow by preparing a plan for future investment in advance. Unfortunately, there exists a high level of uncertainty in the types of transactions that occur in the form of receivables in inter-company transactions, unlike other types of transactions, thereby making the prediction of cash flows difficult. In this study, we analyze the trend of cash flow related to account receivables that may arise between firms, by using a stochastic approach. In particular, we utilize Variable Order Markov (VOM) model to predict how future cash flows will change based on cash flow history. As a result of this study, we show that the average accuracy of the VOM model increases about 12.5% or more compared with that of other existing techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.137-143
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• 2010

A method for improving prediction accuracy through processing time series data has been studied in this research. We have designed techniques to model multiple similar time series data and avoided the shortcomings of single prediction model. We predicted the future changes by effective rules derived from these models. The methods for testing prediction accuracy consists of three types: fixed interval, sliding, and cumulative method. Among the three, cumulative method produced the highest accuracy.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.54-59
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• 2010

This paper suggests a new data link protocol with an adaptive frame length control scheme for wireless data networks which is subject to errors that occur with time variance. We analyze the proposed scheme under a two-state markov block interference(BI) model. Numerical results show that the proposed scheme can achieve high throughput performance for both dense and diffuse burst noise channels.