kneedle: Knee/Elbow Point Detection¶

Description¶

Finds the most significant knee/elbow using the Kneedle algorithm with exponential smoothing.

Usage¶

kneedle_increasing(x, convex = TRUE, dt = 0.01)

Arguments¶


`x`	data vector (increasing)
`convex`	whether the data in `x` are convex-ish (elbow detection) or not (knee lookup)
`dt`	controls the smoothing parameter \(\alpha = 1-\exp(-dt)\) of the exponential moving average, \(y_i = \alpha x_i + (1-\alpha) x_{i-1}\), \(y_1 = x_1\)

Value¶

Returns the index of the knee/elbow point; 1 if not found.

Author(s)¶

Marek Gagolewski

References¶

V. Satopaa, J. Albrecht, D. Irwin, B. Raghavan, Finding a “Kneedle” in a haystack: Detecting knee points in system behavior, In: 31st Intl. Conf. Distributed Computing Systems Workshops, 2011, 166-171, doi:10.1109/ICDCSW.2011.20

See Also¶

The official online manual of deadwood at https://deadwood.gagolewski.com/