Darius Dilijonas

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In this paper two different methods are used to forecast the daily cash demand for automatic teller machines (ATM). The first method is based on flexible artificial neural network (ANN). The generalization properties of this ANN were improved using special adaptive regularization term. The second forecasting method employs the support vector regression(More)
This paper presents an approach to cash management for automatic teller machine (ATM) network. This approach is based on an artificial neural network to forecast a daily cash demand for every ATM in the network and on the optimization procedure to estimate the optimal cash load for every ATM. During the optimization procedure, the most important factors for(More)
The paper presents an artificial neural network based approach in support of cash demand forecasting for automatic teller machine (ATM). On the start phase a three layer feed-forward neural network was trained using Levenberg-Marquardt algorithm and historical data sets. Then ANN was retuned every week using the last observations from ATM. The(More)
As the world economy keeps on changing, financial institutions and investors always look forward to a system by which they can monitor the dynamic financial state of the world. This calls for a system that could simulate and predict financial positions based on financial market trends in order to manage and identify the best package to invest in. The paper(More)
The paper analyses the topic of service system productivity and profitability. Main focus of the research is self-service area, namely, the increase of ATM network productivity. Paper presents performance evaluation of self-service systems and improvement model for its increasing profitability. This model combines internal and external quality criteria and(More)
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