A review of scalable time series pattern recognition

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.