Book Loan Quantity Prediction Using Time Series Data Mining

DOI

10.2991/icectt-15.2015.86How to use a DOI?

Keywords

Digital Library; Book Loan Quantity; Time Series Data Mining; Event prediction

Abstract

This paper shows a new method to book loan quantity prediction using time series data mining which unites data mining and chaos theory to characterize and predict events in nonperiodic, complex and chaotic time series. Intelligent library management, including inquire, borrowing and reading, typify a class of nonlinear systems named chaotic, in which the relationships between variables in a system are disproportionate and dynamic, nevertheless entirely deterministic. Chaos theory offers an integrated explanation for anomalies and irregular behavior in systems that are not internally stochastic. The showed time series data mining technique concentrate on prediction of events where book loan quantity comprises the events in a library daily time series. The technique is demonstrated using data collected at library of Hohai University in China. Results associated with the impact of earliness of prediction and the prediction accuracy vs. acceptable risk-level is presented.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Yuqing Shi
AU  - Yuelong Zhu
PY  - 2015/11
DA  - 2015/11
TI  - Book Loan Quantity Prediction Using Time Series Data Mining
BT  - Proceedings of the 2015 International Conference on Electromechanical Control Technology and Transportation
PB  - Atlantis Press
SP  - 452
EP  - 455
SN  - 2352-5401
UR  - https://doi.org/10.2991/icectt-15.2015.86
DO  - 10.2991/icectt-15.2015.86
ID  - Shi2015/11
ER  -