Title: A review of scalable time series pattern recognition

Authors: Kwan-Hua Sim; Kwan-Yong Sim; Valliappan Raman

Addresses: Swinburne University of Technology, Sarawak Campus, Jalan Simpang Tiga, 93250 Kuching, Sarawak, Malaysia ' Swinburne University of Technology, Sarawak Campus, Jalan Simpang Tiga, 93250 Kuching, Sarawak, Malaysia ' Swinburne University of Technology, Sarawak Campus, Jalan Simpang Tiga, 93250 Kuching, Sarawak, Malaysia

Abstract: Time series data mining helps derive new, meaningful and hidden knowledge from time series data. Thus, time series pattern recognition has been the core functionality in time series data mining applications. However, mining of unknown scalable time series patterns with variable lengths is by no means trivial. It could result in quadratic computational complexities to the search space, which is computationally untenable even with the state-of-the-art time series pattern mining algorithms. The mining of scalable unknown time series patterns also requires the superiority of the similarity measure, which is clearly beyond the comprehension of standard distance measure in time series. It has been a deadlock in the pursuit of a robust similarity measure, while trying to contain the complexity of the time series pattern search algorithm. This paper aims to provide a review of the existing literature in time series pattern recognition by highlighting the challenges and gaps in scalable time series pattern mining.

Keywords: time series pattern recognition; scalable time series pattern matching; motif discovery; time series data mining; distance measure; dimension reduction; sliding window search.

DOI: 10.1504/IJBIDM.2022.125217

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.3, pp.373 - 395

Received: 27 Jan 2021
Accepted: 24 Jun 2021
Published online: 02 Sep 2022 *

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