  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.
