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tsoutliers (version 0.6)

Detection of Outliers in Time Series

Description

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

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Install

install.packages('tsoutliers')

Monthly Downloads

1,525

Version

0.6

License

GPL-2

Maintainer

Javier López-de-Lacalle

Last Published

January 27th, 2015

Functions in tsoutliers (0.6)

bde9915

Data Set: Working Paper bde9915

locate.outliers.loops

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

ipi

Data Set: Industrial Production Indices

tso

Automatic Procedure for Detection of Outliers

plot.tsoutliers

Display Outlier Effects Detected by tsoutliers

outliers.effects

Create the Pattern of Different Types of Outliers

remove.outliers

Stage II of the Procedure: Remove Outliers

tsoutliers-package

Automatic Detection of Outliers in Time Series

outliers.regressors

Regressor Variables for the Detection of Outliers

hicp

Data Set: Harmonised Indices of Consumer Prices

outliers.tstatistics

Test Statistics for the Significance of Outliers

JarqueBera.test

Jarque-Bera Test for Normality

locate.outliers

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

outliers

Define Outliers in a Data Frame

coefs2poly

Product of the Polynomials in an ARIMA Model

calendar.effects

Calendar Effects