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K-Means Clustering with Neural Networks for ATM Cash Repository Prediction


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Optimal forecasting of ATM cash repository in an optimal way is a complex task. This paper deals with cash demand forecasting of NN5 time series data using neural networks. NN5 reduced Dataset is a subsample of 11 time series of complete dataset of 111 daily time series drawn from homogeneous population of empirical cash demand time series. Main objective of this paper is to forecast cash demand forecasting of NN5 data with neural networks. Further, the same process is applied on clusters of ATMs. Discrete time wrapping is used as distance measure. Root mean square error has been calculated for such clustered group of ATMs and average is calculated. Root Mean Square error indicates applications of clustering before applying Neural Network increases precision in forecasting of ATM Cash Repository.

Item Type: Paper presented at a conference, workshop or other event, and published in the proceedings
Uncontrolled Keywords: Clustering, Prediction, Support vector regression, ATM Cash Withdrawal
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
Depositing User: Prashant Khanna
Date Deposited: 16 Nov 2017 03:11
Last Modified: 21 Jul 2023 04:43

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