Online Change Point Detection for Matrix-Valued Time Series

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Documentation for package ‘OLCPM’ version 0.1.2

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cv.table cv.table
gen.data generate data
gen.psi.tau.flat calculate eigenvalue series by "flat" method
gen.psi.tau.proj calculate eigenvalue series by projected method
getcv calculate critical values
impute.linear impute missing entries by linear interpolation
ITP_noproj testing the number of row factors- without projection
ITP_proj testing the number of row factors- with projection
kpe determine factor number - projected
KSTP determine row factor number - test
moment.determine determine the moment (largest) of the data samples
moment.test test whether the k-th moment exists
outlier.remove remove outliers
test.multiple.robust robust test of multiple change point for matrix-valued online time series
test.once.flat test single change point for matrix-valued online time series -"flat" version
test.once.flat.robust robust test of single change point for matrix-valued online time series -"flat" version
test.once.proj test single change point for matrix-valued online time series-projected version
test.once.proj.robust robust test of single change point for matrix-valued online time series-projected version
test.once.psi test single change point for matrix-valued online data given rolling eigenvalue series
test.once.psi.robust robust test of single change point for matrix-valued online data given rolling eigenvalue series
var.exp explanatory power of factors