Authors:
Nour Fodil
1
;
Damien Olivier
1
and
Pierrick Tranouez
2
Affiliations:
1
Litis, University of Le Havre Normandy, Le Havre, France
;
2
Litis, University of Rouen Normandy, Rouen, France
Keyword(s):
Motifs, Time Series, Pattern, Symbolic Representation, SAX, 1d-SAX, fABBA, Sequitur, UniformSAX.
Abstract:
Motif discovery in time series is a process aimed at finding significant original structures. Methods like SAX rely on dimensionality reduction techniques to reduce computation time. Their inability to capture amplitude variations is one of their limitations. By introducing a new representation named UniformSAX, we aim to improve this aspect. We compare our approach to SAX, 1d-SAX, and fABBA, also introducing grammatical inference. The results show that approaches relying exclusively on representations are more suitable for fixed-length motifs but lose effectiveness for variable-length motifs.