| Title: | Time Series Entropies |
| Version: | 0.9 |
| Description: | Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance. |
| Depends: | R (≥ 3.4.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | yes |
| RoxygenNote: | 6.1.0.9000 |
| Author: | Jiri Tomcala [aut, cre] |
| Maintainer: | Jiri Tomcala <jiri.tomcala@vsb.cz> |
| Packaged: | 2018-10-01 08:36:54 UTC; jirka |
| Repository: | CRAN |
| Date/Publication: | 2018-10-08 11:00:07 UTC |
ApEn
Description
This function computes approximate entropy of given time series.
Usage
ApEn(TS, dim = 2, lag = 1, r = 0.2 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.2*sd(TS) |
Examples
timser <- rnorm(2000)
ApEn(timser)
ApEn(timser, r = 0.1*sd(timser))
ApEn(timser, dim = 3, r = 0.1*sd(timser))
ApEn_C
Description
This function computes approximate entropy of given time series. It is implemented in C.
Usage
ApEn_C(TS, dim = 2, lag = 1, r = 0.2 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.2*sd(TS) |
Examples
timser <- rnorm(2000)
ApEn_C(timser)
ApEn_C(timser, r = 0.1*sd(timser))
ApEn_C(timser, dim = 3, r = 0.1*sd(timser))
ApEn_R
Description
This function computes approximate entropy of given time series. It is implemented in R.
Usage
ApEn_R(TS, dim = 2, lag = 1, r = 0.2 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.2*sd(TS) |
Examples
timser <- rnorm(2000)
ApEn_R(timser)
ApEn_R(timser, r = 0.1*sd(timser))
ApEn_R(timser, dim = 3, r = 0.1*sd(timser))
FastApEn
Description
This function computes fast approximate entropy of given time series.
Usage
FastApEn(TS, dim = 2, lag = 1, r = 0.15 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.15*sd(TS) |
Examples
timser <- rnorm(2000)
FastApEn(timser)
FastApEn(timser, r = 0.1*sd(timser))
FastApEn(timser, dim = 3, r = 0.1*sd(timser))
FastApEn_C
Description
This function computes fast approximate entropy of given time series. It is implemented in C.
Usage
FastApEn_C(TS, dim = 2, lag = 1, r = 0.15 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.15*sd(TS) |
Examples
timser <- rnorm(2000)
FastApEn_C(timser)
FastApEn_C(timser, r = 0.1*sd(timser))
FastApEn_C(timser, dim = 3, r = 0.1*sd(timser))
FastApEn_R
Description
This function computes fast approximate entropy of given time series. It is implemented in R.
Usage
FastApEn_R(TS, dim = 2, lag = 1, r = 0.15 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.15*sd(TS) |
Examples
timser <- rnorm(2000)
FastApEn_R(timser)
FastApEn_R(timser, r = 0.1*sd(timser))
FastApEn_R(timser, dim = 3, r = 0.1*sd(timser))
FastSampEn
Description
This function computes fast sample entropy of given time series.
Usage
FastSampEn(TS, dim = 2, lag = 1, r = 0.15 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.15*sd(TS) |
Examples
timser <- rnorm(2000)
FastSampEn(timser)
FastSampEn(timser, r = 0.1*sd(timser))
FastSampEn(timser, dim = 3, r = 0.1*sd(timser))
FastSampEn_C
Description
This function computes fast sample entropy of given time series. It is implemented in C.
Usage
FastSampEn_C(TS, dim = 2, lag = 1, r = 0.15 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.15*sd(TS) |
Examples
timser <- rnorm(2000)
FastSampEn_C(timser)
FastSampEn_C(timser, r = 0.1*sd(timser))
FastSampEn_C(timser, dim = 3, r = 0.1*sd(timser))
FastSampEn_R
Description
This function computes fast sample entropy of given time series. It is implemented in R.
Usage
FastSampEn_R(TS, dim = 2, lag = 1, r = 0.15 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.15*sd(TS) |
Examples
timser <- rnorm(2000)
FastSampEn_R(timser)
FastSampEn_R(timser, r = 0.1*sd(timser))
FastSampEn_R(timser, dim = 3, r = 0.1*sd(timser))
SampEn
Description
This function computes sample entropy of given time series.
Usage
SampEn(TS, dim = 2, lag = 1, r = 0.2 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.2*sd(TS) |
Examples
timser <- rnorm(2000)
SampEn(timser)
SampEn(timser, r = 0.1*sd(timser))
SampEn(timser, dim = 3, r = 0.1*sd(timser))
SampEn_C
Description
This function computes sample entropy of given time series. It is implemented in C.
Usage
SampEn_C(TS, dim = 2, lag = 1, r = 0.2 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.2*sd(TS) |
Examples
timser <- rnorm(2000)
SampEn_C(timser)
SampEn_C(timser, r = 0.1*sd(timser))
SampEn_C(timser, dim = 3, r = 0.1*sd(timser))
SampEn_R
Description
This function computes sample entropy of given time series. It is implemented in R.
Usage
SampEn_R(TS, dim = 2, lag = 1, r = 0.2 * sd(TS))
Arguments
TS |
- given time series |
dim |
- dimension of given time series, default value is 2 |
lag |
- downsampling, default value is 1 |
r |
- radius of searched areas, default value is 0.2*sd(TS) |
Examples
timser <- rnorm(2000)
SampEn_R(timser)
SampEn_R(timser, r = 0.1*sd(timser))
SampEn_R(timser, dim = 3, r = 0.1*sd(timser))