Transformer (Pairwise Panel)

LcssTslearn

Multivariate Unequal length Missing values

Longest Common Subsequence similarity distance, from tslearn.

Direct interface to tslearn.metrics.lcss.

Quickstart

python

from sktime.dists_kernels.lcss import LcssTslearn

estimator = LcssTslearn(eps=1.0, global_constraint=None, sakoe_chiba_radius=None, itakura_max_slope=None)

Parameters(4)

epsfloat (default: 1.): Maximum matching distance threshold.
global_constraint{“itakura”, “sakoe_chiba”} or None (default: None): Global constraint to restrict admissible paths for DTW.
sakoe_chiba_radiusint or None (default: None): Radius to be used for Sakoe-Chiba band global constraint. If None and global_constraint is set to "sakoe_chiba", a radius of 1 is used. If both sakoe_chiba_radius and itakura_max_slope are set, global_constraint is used to infer which constraint to use among the two. In this case, if global_constraint corresponds to no global constraint, a RuntimeWarning is raised and no global constraint is used.
itakura_max_slopefloat or None (default: None): Maximum slope for the Itakura parallelogram constraint. If None and global_constraint is set to "itakura", a maximum slope of 2 is used. If both sakoe_chiba_radius and itakura_max_slope are set, global_constraint is used to infer which constraint to use among the two. In this case, if global_constraint corresponds to no global constraint, a RuntimeWarning is raised and no global constraint is used.

References

[1]

M. Vlachos, D. Gunopoulos, and G. Kollios. 2002. “Discovering Similar Multidimensional Trajectories”, In Proceedings of the 18th International Conference on Data Engineering (ICDE ‘02). IEEE Computer Society, USA, 673.