mass: Mueen's ultra-fast Algorithm for Similarity Search (MASS)

In matrixProfile: Matrix Profile

Description

Calculates a sliding dot prodocts of given data.

Usage

mass(q, t)

Arguments

q	A query data for dot product.
t	A timeseries data for analysis.

Value

Dot products between query and all subsequences in Timeseries. In the paper, we can implicitly construct a distance matrix with this output values that are the result of these dot products.

Author(s)

Donghwan Kim
[e-mail] [e-mail] [e-mail]

References

Yeh, C. C. M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H. A., ... & Keogh, E. (2016) <DOI:10.1109/ICDM.2016.0179>. 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, 2016, pp. 1317-1322.
https://www.cs.unm.edu/~mueen/MASS_V2.m

Examples

dt = AirPassengers
dt = as.vector(dt)
par(mfrow = c(2,1))
plot(dt, type = "l")
dm <- mass(q = dt[1:10], t = dt[-c(1:10)])
plot(dm, tyep = "l")

Example output

Loading required package: TTR
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Loading required package: fftw
Loading required package: signal

Attaching package: ‘signal’

The following objects are masked from ‘package:stats’:

    filter, poly

matrixProfile documentation built on May 2, 2019, 9:43 a.m.