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tsoutliers (version 0.5)

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

0.5

License

GPL-2

Maintainer

Javier López-de-Lacalle

Last Published

January 25th, 2015

Functions in tsoutliers (0.5)

remove.outliers

Stage II of the Procedure: Remove Outliers

outliers.tstatistics

Test Statistics for the Significance of Outliers

tsoutliers-package

Automatic Detection of Outliers in Time Series

calendar.effects

Calendar Effects

bde9915

Data Set: Working Paper bde9915

plot.tsoutliers

Display Outlier Effects Detected by tsoutliers

ipi

Data Set: Industrial Production Indices

coefs2poly

Product of the Polynomials in an ARIMA Model

JarqueBera.test

Jarque-Bera Test for Normality

locate.outliers.loops

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

outliers.regressors

Regressor Variables for the Detection of Outliers

outliers.effects

Create the Pattern of Different Types of Outliers

outliers

Define Outliers in a Data Frame

hicp

Data Set: Harmonised Indices of Consumer Prices

locate.outliers

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

tso

Automatic Procedure for Detection of Outliers