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tsoutliers (version 0.6-8)

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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Install

install.packages('tsoutliers')

Monthly Downloads

2,704

Version

0.6-8

License

GPL-2

Maintainer

Javier López-de-Lacalle

Last Published

February 24th, 2019

Functions in tsoutliers (0.6-8)

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)