Description Details Author(s) References See Also Examples

Detects multiple changepoints in uni- or multivariate time series data. The algorithm is based on Bayesian methods and detects changes on-line; ie the model updates with every observation rather than relying on retrospective segmentation. However, the user may choose to use the algorithm off- or on-line.

The DESCRIPTION file: Detects multiple changepoints in uni- or multivariate time series data. The algorithm is based on Bayesian methods and detects changes on-line; ie the model updates with every observation rather than relying on retrospective segmentation. However, the user may choose to use the algorithm off- or on-line.

This package provides two ways to use the oCPD algorithm; first is the function `offlineCPD`

, where you provide your whole time series at once. The algorithm still performs as if it were being provided data points one at a time, but it's more convenient for certain analysis. Second, there is `onlineCPD`

, where you provide your time series one point at a time, as the oCPD algorithm is meant to be performed.

BETA Release

Zachary Zanussi <[email protected]>

Maintainer: Zachary Zanussi <[email protected]>

Adams, R. P. and Mackay, D. J. C. (2007), Bayesian Online Changepoint Detection

###OUR PAPER,WHEN IT EXISTS

onlineCPD

1 2 3 4 5 6 7 8 9 | ```
##### Univariate Data #####
set.seed(6)
x <- c(rnorm(50,mean=0.3,sd=0.15),rnorm(40,mean=0.7,sd=0.1),rnorm(60,mean=0.5,sd=0.15))
plot(offlineCPD(x))
##### Real Multivariate Data #####
data(WalBelSentiment)
data(WalBelTimes)
plot(offlineCPD(WalBelSentiment[1400:1600,],WalBelTimes[1400:1600]))
``` |

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