Multiobjective time series matching

We present here an innovative problem that can be casted into a new approach merging multiobjective optimization and time series matching algorithms, called //MultiObjective Time Series// (MOTS) matching. We formally state this novel problem that could lead to a whole range of applications in several fields of research and report an efficient implementation. This approach allowed in the scope of sound samples querying to cope with the multidimensional nature of timbre perception and also to obtain a whole set of efficient propositions rather than a single best solution.