A New Pattern Representation Method for Time-Series Data

Roonak Rezvani,Payam Barnaghi,Shirin Enshaeifar

doi:10.1109/tkde.2019.2961097

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 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.

Highlights

T HE rapid growth of connected devices and networks generates massive amount of data
We have shown that our representation is more efficient in comparison with other representation methods
The results of the Lagrangian multiplier provide vector representations which can be used to analyse patterns and changes in time-series data

Summary

Introduction

T HE rapid growth of connected devices and networks generates massive amount of data. Some of this data is generated by Internet of Things (IoT) technologies which capture information from the physical environment. IoT data is often represented as time-series which is a collection of observations in a time domain [1]. Analysing time-series data can be beneficial in developing effective methods for processing the observations and gaining insight into relationships and hidden structures of the data. One of the key steps in processing time-series data is often dimensionality reduction and applying spatial methods to transform the data from time domain to other domains [2] such as Discrete

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Conclusion