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tsoutliers (version 0.6-10)

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

2,638

Version

0.6-10

License

GPL-2

Maintainer

Javier López-de-Lacalle

Last Published

February 12th, 2024

Functions in tsoutliers (0.6-10)

plot.tsoutliers

Display Outlier Effects Detected by tsoutliers
outliers

Define Outliers in a Data Frame
tsoutliers-package

Automatic Detection of Outliers in Time Series
outliers.effects

Create the Pattern of Different Types of Outliers
print.tsoutliers

Print tsoutliers object
locate.outliers.loops

Stage I of the Procedure: Locate Outliers (Loop Around Functions)
remove.outliers-deprecated

Stage II of the Procedure: Discard Outliers
hicp

Data Set: Harmonised Indices of Consumer Prices
tso

Automatic Procedure for Detection of Outliers
outliers.regressors

Regressor Variables for the Detection of Outliers
locate.outliers

Stage I of the Procedure: Locate Outliers (Baseline Function)
outliers.tstatistics

Test Statistics for the Significance of Outliers
coefs2poly

Product of the Polynomials in an ARIMA Model
bde9915

Data Set: Working Paper ‘bde9915’
ipi

Data Set: Industrial Production Indices
find.consecutive.outliers

Find outliers at consecutive time points
JarqueBera.test

Jarque-Bera Test for Normality
discard.outliers

Stage II of the Procedure: Discard Outliers
calendar.effects

Calendar Effects