tsoutliers v0.6-8

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by Javier López-de-Lacalle View Source

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.

Functions in tsoutliers

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

Name
tsoutliers-intro.Rnw
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Details

Date	2019-02-24
NeedsCompilation	no
Encoding	UTF-8
License	GPL-2
URL	https://jalobe.com
Packaged	2019-02-24 18:12:39 UTC; javlacalle
Repository	CRAN
Date/Publication	2019-02-24 22:00:03 UTC

imports	forecast , stats
depends	R (>= 3.0.0)
Contributors	Javier López-de-Lacalle