Logo image
Open Research University homepage
Surrey researchers Sign in
Pattern Identification for State Prediction in Dynamic Data Streams
Conference presentation   Open access   Peer reviewed

Pattern Identification for State Prediction in Dynamic Data Streams

Shirin Enshaeifar, Seyed Hoseinitabatabaei, Alireza Ahrabian and Payam Barnaghi
The 10th International Conference on Internet of Things (iThings 2017) (Exeter, UK, 21/06/2017–23/06/2017)
21/06/2017

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.
pdf
Pattern Identification263.79 kBDownloadView
TextSRIDA Open Access
url
http://cse.stfx.ca/~iThings2017/View
Organisation

Metrics

Details

Logo image

Usage Policy