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tsoutliers (version 0.6-6)

Detection of Outliers in Time Series

Description

Detection of outliers in time series following the Chen and Liu (1993) procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.

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Version

Install

install.packages('tsoutliers')

Monthly Downloads

3,523

Version

0.6-6

License

GPL-2

Maintainer

Javier López-de-Lacalle

Last Published

May 27th, 2017

Functions in tsoutliers (0.6-6)

bde9915

Data Set: Working Paper ‘bde9915’
calendar.effects

Calendar Effects
locate.outliers.loops

Stage I of the Procedure: Locate Outliers (Loop Around Functions)
locate.outliers

Stage I of the Procedure: Locate Outliers (Baseline Function)
coefs2poly

Product of the Polynomials in an ARIMA Model
discard.outliers

Stage II of the Procedure: Discard Outliers
find.consecutive.outliers

Find outliers at consecutive time points
hicp

Data Set: Harmonised Indices of Consumer Prices
JarqueBera.test

Jarque-Bera Test for Normality
outliers.regressors

Regressor Variables for the Detection of Outliers
remove.outliers-deprecated

Stage II of the Procedure: Discard Outliers
tsoutliers-package

Automatic Detection of Outliers in Time Series
tso

Automatic Procedure for Detection of Outliers
outliers

Define Outliers in a Data Frame
outliers.effects

Create the Pattern of Different Types of Outliers
ipi

Data Set: Industrial Production Indices
outliers.tstatistics

Test Statistics for the Significance of Outliers
plot.tsoutliers

Display Outlier Effects Detected by tsoutliers
print.tsoutliers

Print tsoutliers object