| Type: | Package |
| Title: | Locating Distributional Changes in Highly Dependent Time Series |
| Version: | 1.0.3 |
| Maintainer: | Lukas Zierahn <lukas@kappa-mm.de> |
| Description: | Provides algorithms to locate multiple distributional change-points in piecewise stationary time series. The algorithms are provably consistent, even in the presence of long-range dependencies. Knowledge of the number of change-points is not required. The code is written in Go and interfaced with R. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| URL: | https://github.com/azalk/GoChest |
| BugReports: | https://github.com/azalk/GoChest/issues |
| Imports: | Rdpack, reticulate |
| Suggests: | testthat |
| RdMacros: | Rdpack |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2021-02-13 15:46:50 UTC; lukas |
| Author: | Lukas Zierahn [cre, aut], Azadeh Khaleghi [aut] |
| Repository: | CRAN |
| Date/Publication: | 2021-02-13 16:00:02 UTC |
find_changepoints
Description
Returns the position of changepoints in the sequence. NOTE: PyChest needs to be installed first by calling ‘install_PyChest’.
Usage
find_changepoints(sample, minimum_distance, process_count)
Arguments
sample |
A vector of floats corresponding to the piecewise stationary sample where the retrospective changes are to be sought |
minimum_distance |
A real number between 0 and 1 corresponding to a lower-bound on the minimum normalized length of the stationary segments (as percentage of total sample length) |
process_count |
The different number of distinct stationary processes present. |
Value
The list of changepoints in increasing size
References
Khaleghi A, Ryabko D (2014). “Asymptotically consistent estimation of the number of change points in highly dependent time series.” In International Conference on Machine Learning, 539–547.
Khaleghi A, Ryabko D (2012). “Locating changes in highly dependent data with unknown number of change points.” In Advances in Neural Information Processing Systems, 3086–3094.
install_PyChest
Description
Initializes the package and installs/updates PyChest into the local reticulate-Python environment
Usage
install_PyChest()
Value
No return value, called to install the PyChest Package
list_estimator
Description
Returns the position of changepoints in the sequence. NOTE: PyChest needs to be installed first by calling ‘install_PyChest’.
Usage
list_estimator(sample, minimum_distance)
Arguments
sample |
A vector of floats corresponding to the piecewise stationary sample where the retrospective changes are to be sought |
minimum_distance |
A real number between 0 and 1 corresponding to a lower-bound on the minimum normalized length of the stationary segments (as percentage of total sample length) |
Value
The list of changepoints in order of score
References
Khaleghi A, Ryabko D (2012). “Locating changes in highly dependent data with unknown number of change points.” In Advances in Neural Information Processing Systems, 3086–3094.