Fuzzy Longest Common Subsequence Matching With FCM Using R
This work addresses time series analysis for researchers, but appears incremental as it builds on existing Longest Common Subsequence methods with fuzzy extensions.
The paper tackles the problem of finding common patterns in real-valued time series by proposing a Fuzzy Longest Common Subsequence matching method, aiming to improve similarity measurement through abstraction and fuzzy logic.
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.