Package: tsoutliers 0.6-10

Javier López-de-Lacalle

tsoutliers: Detection of Outliers in Time Series

Detection of outliers in time series following the Chen and Liu (1993) <doi:10.2307/2290724> procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.

Authors:Javier López-de-Lacalle <[email protected]>

tsoutliers_0.6-10.tar.gz
tsoutliers_0.6-10.tar.gz(r-4.5-noble)tsoutliers_0.6-10.tar.gz(r-4.4-noble)
tsoutliers_0.6-10.tgz(r-4.4-emscripten)tsoutliers_0.6-10.tgz(r-4.3-emscripten)
tsoutliers.pdf |tsoutliers.html✨
tsoutliers/json (API)
NEWS

# Install 'tsoutliers' in R:

install.packages('tsoutliers', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

bde9915 - Data Set: Working Paper 'bde9915'
hicp - Data Set: Harmonised Indices of Consumer Prices
ipi - Data Set: Industrial Production Indices

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

4.66 score 5 stars 5 packages 2.0k downloads 3 mentions 17 exports 45 dependencies

Last updated 10 months agofrom:130b6315f4. Checks:OK: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 09 2024
R-4.5-linux	OK	Dec 09 2024

Exports:calendar.effects coefs2poly discard.outliers find.consecutive.outliers JarqueBera.test locate.outliers locate.outliers.iloop locate.outliers.oloop outliers outliers.effects outliers.regressors outliers.tstatistics plot.tsoutliers print.tsoutliers remove.outliers tso tso0

Dependencies:cli colorspace curl fansi farver forecast fracdiff generics ggplot2 glue gtable isoband jsonlite labeling lattice lifecycle lmtest magrittr MASS Matrix mgcv munsell nlme nnet pillar pkgconfig quadprog quantmod R6 RColorBrewer Rcpp RcppArmadillo rlang scales tibble timeDate tseries TTR urca utf8 vctrs viridisLite withr xts zoo

tsoutliers-intro

Rendered fromtsoutliers-intro.Rnwusingutils::Sweaveon Dec 09 2024.

Last update: 2019-02-24
Started: 2015-01-25

Citation

Readme and manuals

Help Manual

Help page	Topics
Automatic Detection of Outliers in Time Series	_PACKAGE
Data Set: Working Paper 'bde9915'	bde9915
Calendar Effects	calendar.effects
Product of the Polynomials in an ARIMA Model	coefs2poly
Stage II of the Procedure: Discard Outliers	discard.outliers
Find outliers at consecutive time points	find.consecutive.outliers
Data Set: Harmonised Indices of Consumer Prices	hicp
Data Set: Industrial Production Indices	ipi
Jarque-Bera Test for Normality	JarqueBera.test print.mhtest
Stage I of the Procedure: Locate Outliers (Baseline Function)	locate.outliers
Stage I of the Procedure: Locate Outliers (Loop Around Functions)	locate.outliers.iloop locate.outliers.oloop
Define Outliers in a Data Frame	outliers
Create the Pattern of Different Types of Outliers	outliers.effects
Regressor Variables for the Detection of Outliers	outliers.regressors
Test Statistics for the Significance of Outliers	outliers.tstatistics
Display Outlier Effects Detected by 'tsoutliers'	plot.tsoutliers
Print 'tsoutliers' object	print.tsoutliers
Stage II of the Procedure: Discard Outliers	remove.outliers
Automatic Procedure for Detection of Outliers	tso tso0