# tslearn.metrics.lb_envelope¶

tslearn.metrics.lb_envelope(ts, radius=1)[source]

Compute time-series envelope as required by LB_Keogh.

LB_Keogh was originally presented in [1].

Parameters: ts : array-like Time-series for which the envelope should be computed. radius : int (default: 1) Radius to be used for the envelope generation (the envelope at time index i will be generated based on all observations from the time series at indices comprised between i-radius and i+radius). array-like Lower-side of the envelope. array-like Upper-side of the envelope.

lb_keogh
Compute LB_Keogh similarity

References

 [1] Keogh, E. Exact indexing of dynamic time warping. In International Conference on Very Large Data Bases, 2002. pp 406-417.

Examples

>>> ts1 = [1, 2, 3, 2, 1]
>>> env_low, env_up = lb_envelope(ts1, radius=1)
>>> env_low
array([[1.],
[1.],
[2.],
[1.],
[1.]])
>>> env_up
array([[2.],
[3.],
[3.],
[3.],
[2.]])