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2018 | OriginalPaper | Chapter

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

Authors : Pankaj Kumar Jadwal, Sonal Jain, Umesh Gupta, Prashant Khanna

Published in: Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
K-Means Clustering with Neural Networks for ATM Cash Repository Prediction
Authors
Pankaj Kumar Jadwal
Sonal Jain
Umesh Gupta
Prashant Khanna
Copyright Year
2018
Book
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1

Print ISBN: 978-3-319-63672-6

Electronic ISBN: 978-3-319-63673-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-63673-3_71

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