Fuzzy Longest Common Subsequence Matching With FCM Using R

TL;DR

This paper introduces a fuzzy extension of the Longest Common Subsequence algorithm to better identify similar patterns in real-valued time series by incorporating abstraction and symbolic representation.

Contribution

It proposes a novel Fuzzy Longest Common Subsequence matching method tailored for time series analysis, enhancing pattern similarity detection.

Findings

Improved pattern matching accuracy in real-valued time series

Enhanced abstraction of time series through fuzzy logic

Potential for better similarity measures in time series analysis

Abstract

Capturing the interdependencies between real valued time series can be achieved by finding common similar patterns. The abstraction of time series makes the process of finding similarities closer to the way as humans do. Therefore, the abstraction by means of a symbolic levels and finding the common patterns attracts researchers. One particular algorithm, Longest Common Subsequence, has been used successfully as a similarity measure between two sequences including real valued time series. In this paper, we propose Fuzzy Longest Common Subsequence matching for time series.