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A New Pattern Representation Method for Time-series Data

Rezvani, Roonak, Barnaghi, Payam and Enshaeifar, Shirin (2019) A New Pattern Representation Method for Time-series Data IEEE Transactions on Knowledge and Data Engineering.

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Abstract

The rapid growth of Internet of Things (IoT) and sensing technologies has led to an increasing interest in time-series data analysis. In many domains, detecting patterns of IoT data and interpreting these patterns are challenging issues. There are several methods in time-series analysis that deal with issues such as volume and velocity of IoT data streams. However, analysing the content of the data streams and extracting insights from dynamic IoT data is still a challenging task. In this paper, we propose a pattern representation method which represents time-series frames as vectors by first applying Piecewise Aggregate Approximation (PAA) and then applying Lagrangian Multipliers. This method allows representing continuous data as a series of patterns that can be used and processed by various higher-level methods. We introduce a new change point detection method which uses the constructed patterns in its analysis. We evaluate and compare our representation method with Blocks of Eigenvalues Algorithm (BEATS) and Symbolic Aggregate approXimation (SAX) methods to cluster various datasets. We have also evaluated our proposed change detection method. We have evaluated our algorithm using UCR time-series datasets and also a healthcare dataset. The evaluation results show significant improvements in analysing time-series data in our proposed method.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
NameEmailORCID
Rezvani, Roonakr.rezvani@surrey.ac.uk
Barnaghi, PayamP.Barnaghi@surrey.ac.uk
Enshaeifar, Shirins.enshaeifar@surrey.ac.uk
Date : 23 December 2019
Funders : UK Dementia Research Institute, TIHM for Dementia project, European Commissions Horizon 2020 (EU H2020)
DOI : 10.1109/TKDE.2019.2961097
Copyright Disclaimer : © 2019 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.
Uncontrolled Keywords : Lagrangian Multiplier; Data analytics; Aggregation; Data representation; Change point detection
Additional Information : This work is supported by Care Research and Technology Centre at the UK Dementia Research Institute and TIHM for Dementia project. The work is also partially supported by the European Commissions Horizon 2020 (EU H2020) IoTCrawler project (http://iotcrawler.eu/) under contract number: 779852.
Depositing User : Diane Maxfield
Date Deposited : 21 Jan 2020 14:59
Last Modified : 21 Jan 2020 14:59
URI: http://epubs.surrey.ac.uk/id/eprint/853358

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