crossCorNormalized: Normalized Cross-Correlation Function
In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series
Description
Usage
Arguments
Value
References
See Also
Description
Computes the cross-correlation function between two vectors and normalizes
it with the l2 norms of the vectors (so values are between -1 and 1). Internally,
convolution and FFT are used as described by Paparrizos and Gravano (2015).
Usage
1
crossCorNormalized(x, y)
Arguments
x
1st numeric vector/time series.
y
2nd numeric vector/time series.
Value
The normalized cross-correlation as vector (length equals
|x| + |y| - 1.
References
Paparrizos, J. & Gravano, L. (2015). K-shape: Efficient and accurate clustering
of time series. In Proceedings of the 2015 acm sigmod international
conference on management of data (pp. 1855–1870). ACM.
See Also
Other cross-correlation functions: shapeBasedDistance
Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.
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Description Usage Arguments Value References See Also
Description
Computes the cross-correlation function between two vectors and normalizes it with the l2 norms of the vectors (so values are between -1 and 1). Internally, convolution and FFT are used as described by Paparrizos and Gravano (2015).
Usage
1 | crossCorNormalized(x, y)
|
Arguments
x |
1st numeric vector/time series. |
y |
2nd numeric vector/time series. |
Value
The normalized cross-correlation as vector (length equals |x| + |y| - 1.
References
Paparrizos, J. & Gravano, L. (2015). K-shape: Efficient and accurate clustering of time series. In Proceedings of the 2015 acm sigmod international conference on management of data (pp. 1855–1870). ACM.
See Also
Other cross-correlation functions: shapeBasedDistance
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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