PAA_fast: Piecewise Aggregate Approximation

In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series

Description

Divides a time series into windowCount frames of equal length and represents each interval by its mean. If the time series length is not divisible by the number of windows, one element might belong to two windows, but only with a certain fraction to each. Inspired by the pure R implementation in TSclust::PAA(), but faster because C++ code.

Usage

PAA_fast(x, windowCount)

Arguments

x	Numeric vector/time series.
windowCount	The number of windows for the shortened time series.

Value

A numeric vector of the length windowCount.

References

Keogh, E. J. & Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. In Proceedings of the sixth acm sigkdd international conference on knowledge discovery and data mining (pp. 285-289). ACM.

Keogh, E., Chakrabarti, K., Pazzani, M. & Mehrotra, S. (2001). Dimensionality reduction for fast similarity search in large time series databases. Knowledge and information Systems, 3(3), 263-286.

See Also

Other piecewise aggregation functions: PKurtAA_fast, PMaxAA_fast, PMedAA_fast, PMinAA_fast, PSDAA_fast, PSkewAA_fast

Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.