| Type: | Package |
| Title: | Matrix Profile |
| Version: | 0.5.0 |
| Date: | 2018-08-14 |
| Author: | Donghwan Kim |
| Maintainer: | Donghwan Kim <donhkim9714@korea.ac.kr> |
| Description: | A simple and the early stage package for matrix profile based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh (2016) <doi:10.1109/ICDM.2016.0179>. This package calculates all-pairs-similarity for a given window size for time series data. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | yes |
| Repository: | CRAN |
| URL: | https://github.com/ainsuotain/matrixprofile |
| Depends: | R (≥ 3.0.0), graphics, stats, TTR, zoo, fftw, signal |
| NeedsCompilation: | no |
| Packaged: | 2018-08-17 00:46:05 UTC; David |
| Date/Publication: | 2018-08-17 09:20:07 UTC |
Mueen's ultra-fast Algorithm for Similarity Search (MASS)
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
ainsuotain@hanmail.net
donhkim9714@korea.ac.kr
dhkim2@bistel.com
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")
Moving mean
Description
Calculates moving mean of input data.
Usage
movmean(x = x, w = w)
Arguments
x |
A given input data. |
w |
A sliding window of length w. |
Value
An array of local w-point mean values, where each mean is calculated over a sliding window of length w across neighboring elements of x. The window size is automatically truncated at the endpoints when there are not enough elements to fill the window. When the window is truncated, the average is taken over only the elements that fill the window. Output is the same size as x.
Author(s)
Donghwan Kim
ainsuotain@hanmail.net
donhkim9714@korea.ac.kr
dhkim2@bistel.com
References
https://kr.mathworks.com/help/matlab/ref/movmean.html?lang=en
Examples
x <- 1:10
movmean(x, 3)
Moving std
Description
Calculates moving sample standard deviation of input data.
Usage
movstd(x = x, w = w)
Arguments
x |
A given input data. |
w |
A sliding window of length w. |
Value
An array of local w-point sample standard deviation values, where each sample standard deviation is calculated over a sliding window of length w across neighboring elements of x. The window size is automatically truncated at the endpoints when there are not enough elements to fill the window. When the window is truncated, the standard deviation is taken over only the elements that fill the window. Output is the same size as x.
Author(s)
Donghwan Kim
ainsuotain@hanmail.net
donhkim9714@korea.ac.kr
dhkim2@bistel.com
References
https://kr.mathworks.com/help/matlab/ref/movstd.html?lang=en
Examples
x <- 1:10
movstd(x, 3)
Scalable Time series Anytime Matrix Profile (stamp)
Description
Calculates a matrix profile of given data using STAMP algorithm.
Usage
stamp(q, t, by = 10, isPlot = FALSE)
Arguments
q |
A query data for dot product. |
t |
A timeseries data for analysis. |
by |
A parameter that indicates the progress of the process in the process of calculating the matrix profile. For example, if |
isPlot |
A parameter that determines whether or not to draw a plot in the middle of calculating a matrix profile. The default value is |
Details
The matrix profile is calculated by the self join method using the STAMP algorithm. One of the key features of the STAMP algorithm is the attribute anytime. In other words, because the matrix profile is computed rather than randomly, the computation speed is the same, but it is quickly optimized.
Value
An object of class stamp.models.
MP |
A matrix profile computed by given data. |
MPI |
A matrix profile index computed by given data. |
MTI |
A motif index of matrix profile. Unlike in the original paper, it denotes the pair of motif index with the smallest value of matrix profile. |
Note
This package is an early version and will be updated in the neae future. Also note that it is very slow for data with more than 10,000 data points. Since it is not optimized basic functions(e.g. movmean, movstd) for computation and is due to R's own limitations.
Author(s)
Donghwan Kim
ainsuotain@hanmail.net
donhkim9714@korea.ac.kr
dhkim2@bistel.com
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.
http://www.cs.ucr.edu/~eamonn/MatrixProfile.html
See Also
mass (in package matrixProfile)
Examples
# data input
dt = AirPassengers
dt = as.vector(dt)
# generates matrix profile
stamp <- stamp(q = dt[1:12], t = dt[-c(1:12)])
# plotting
par(mfrow = c(2,1))
plot(dt, type = "l", main = "Original Timeseries")
plot(stamp$MP, type = "l", main = "Matrix Profile", xlim = c(0, length(dt)))
Sample standard deviation
Description
Calculates sample standard deviation of input data.
Usage
std(x)
Arguments
x |
A given input data. |
Details
This function is slightly different from the base function sd.
Value
An sample standard deviation of given data.
Author(s)
Donghwan Kim
ainsuotain@hanmail.net
donhkim9714@korea.ac.kr
dhkim2@bistel.com
References
https://en.wikipedia.org/wiki/Standard_deviation
Examples
x <- 1:10
sd(x) # for comparison
std(x) # see difference