Package: AnomalyScore 0.1

Guillermo Granados

AnomalyScore: Anomaly Scoring for Multivariate Time Series

Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.

Authors:Guillermo Granados [aut, cre]

AnomalyScore_0.1.tar.gz
AnomalyScore_0.1.tar.gz(r-4.5-noble)AnomalyScore_0.1.tar.gz(r-4.4-noble)
AnomalyScore_0.1.tgz(r-4.4-emscripten)AnomalyScore_0.1.tgz(r-4.3-emscripten)
AnomalyScore.pdf |AnomalyScore.html✨
AnomalyScore/json (API)

# Install 'AnomalyScore' in R:
install.packages('AnomalyScore', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 1 scripts 562 downloads 29 exports 33 dependencies

Last updated 1 months agofrom:a7a41fd001. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 22 2024
R-4.5-linuxNOTEDec 22 2024

Exports:all_bandsAnomalyscoresframeCortDEcortDEcortNormdistance_matrix_banddepthdistance_matrix_CGCIdistance_matrix_coherencedistance_matrix_cortdistance_matrix_cortNormdistance_matrix_dtwdistance_matrix_mahalanobisdistance_matrix_mvLWSdistance_matrix_PDCdistance_matrix_PMIMEdistance_matrix_RGPDCdistance_matrix_wassersteinDTWcortdxy_bandsinformative_bandskneighbors_distance_docallmatrix_PDCmBTSmBTS_Af_matmBTSCGCImBTSRGPDCmultilagmatrixPMIMEsqnorms

Dependencies:astsaclasscurldata.tabledotCall64dtwfieldsjsonlitelatticeleapslocfitmapsmarimaMASSMatrixmgcvmvLSWnlmeproxyquadprogquantmodRANNRcppRcppEigenspamtransportTSAtseriesTTRviridisLitewavethreshxtszoo

Readme and manuals

Help Manual

Help pageTopics
Pairwise band generation in a multivariate time seriesall_bands
Anomaly score computation for a set of distancesAnomalyscoresframe
Temporal correlation coefficientCort
Distance based on value and behavior of the time seriesDEcort
Normalized version of the Cort distance the modification is based on using the coefficient of variation rather than euclidean distance, performed by normalizing by the absolute value of the total differences of the series.DEcortNorm
Pairwise distance matrix based on the band depth distancedistance_matrix_banddepth
Pairwise distance matrix based on the conditional Granger causality indexdistance_matrix_CGCI
Distance matrix from a coherence measuredistance_matrix_coherence
Distance matrix from a pattern recognition distancedistance_matrix_cort
Normalized distance matrix from a pattern recognition distancedistance_matrix_cortNorm
Normalized distance matrix from dynamic time-warping distancedistance_matrix_dtw
Pairwise distance matrix based on the mahalanobis distancedistance_matrix_mahalanobis
Pairwise distance matrix based on the multivariate locally wavelet partial coherencedistance_matrix_mvLWS
Distance matrix from a partial directed coherence measure (PDC)distance_matrix_PDC
Pairwise distance matrix based on the partial mutual information of mixed embedings (PMIME) methoddistance_matrix_PMIME
Pairwise distance matrix based on the restricted generalized partial directed coherencedistance_matrix_RGPDC
Distance matrix from based on the Wasserstein distancedistance_matrix_wasserstein
Extention of the dynamic time warping distanceDTWcort
Band depth distance between 2 time series given a set of bandsdxy_bands
indexes where a series is within a specific bandinformative_bands
K-Nearest neighbors algorithm to compute an anomaly scorekneighbors_distance_docall
Partial directed coherence matrixmatrix_PDC
modified Back-in-time Selection for vector AR parameters estimationmBTS
mBTS Vector Autoregressive coefficients fourier transformmBTS_Af_mat
computation of the conditional Granger causality indexmBTSCGCI
Restricted Generalized Partial Directed CoherencemBTSRGPDC
multilagmatrixmultilagmatrix
PMIME Partial mutual information from mixed embeddingPMIME
Quadratic multiplication of a matrix M with respect to a matrix A: Conj(M) A M, where Conj() is the complex conjugate functionsqnorms