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Pattern Identification for State Prediction in Dynamic Data Streams

Enshaeifar, Shirin, Hoseinitabatabaei, Seyed, Ahrabian, Alireza and Barnaghi, Payam (2017) Pattern Identification for State Prediction in Dynamic Data Streams In: The 10th International Conference on Internet of Things (iThings 2017), 21-23 Jun 2017, Exeter, UK.

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Abstract

This work proposes a pattern identification and online prediction algorithm for processing Internet of Things (IoT) time-series data. This is achieved by first proposing a new data aggregation and datadriven discretisation method that does not require data segment normalisation. We apply a dictionary based algorithm in order to identify patterns of interest along with prediction of the next pattern. The performance of the proposed method is evaluated using synthetic and real-world datasets. The evaluations results shows that our system is able to identify the patterns by up to 85% accuracy which is 16.5% higher than a baseline using the Symbolic Aggregation Approximation (SAX) method.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Enshaeifar, Shirins.enshaeifar@surrey.ac.ukUNSPECIFIED
Hoseinitabatabaei, Seyeda.taba@surrey.ac.ukUNSPECIFIED
Ahrabian, Alirezaa.ahrabian@surrey.ac.ukUNSPECIFIED
Barnaghi, PayamP.Barnaghi@surrey.ac.ukUNSPECIFIED
Date : 21 June 2017
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Clive Harris
Date Deposited : 06 Jun 2017 15:01
Last Modified : 06 Jun 2017 15:01
URI: http://epubs.surrey.ac.uk/id/eprint/841315

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