A neural network for mining large volumes of time

series data

B. Liang; J. Austin

doi:10.1109/ICIT.2005.1600724

A neural network for mining large volumes of time series data

B. Liang, J. Austin

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented.

Original language	English
Title of host publication	IEEE International Conference on Industrial Technology (ICIT) 2005 (14-17 December 2005, City University of Hong Kong)
Place of Publication	New York
Publisher	IEEE
Pages	688-693
Number of pages	5
Volume	1-2
ISBN (Print)	0-7803-9484-4
DOIs	https://doi.org/10.1109/ICIT.2005.1600724
Publication status	Published - Dec 2005

Bibliographical note

© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Access to Document

10.1109/ICIT.2005.1600724

austinj2.pdf

http://dx.doi.org/10.1109/ICIT.2005.1600724

Cite this

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