tsoutliers v0.6-8


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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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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

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