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tsoutliers (version 0.4)

Automatic Detection of Outliers in Time Series

Description

This package implements a procedure based on the approach described in Chen and Liu (1993) for automatic detection of outliers in time series. Innovational 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

0.4

License

GPL-2

Maintainer

Javier López-de-Lacalle

Last Published

June 27th, 2014

Functions in tsoutliers (0.4)

remove.outliers

Stage II of the Procedure: Remove Outliers

Data Set: Industrial Production Indices

outliers.regressors

Regressor Variables for the Detection of Outliers

calendar.effects

Calendar Effects

Define Outliers in a Data Frame

outliers.tstatistics

Test Statistics for the Significance of Outliers

outliers.effects

Create the Pattern of Different Types of Outliers

JarqueBera.test

Jarque-Bera Test for Normality

Automatic Procedure for Detection of Outliers

Data Set: Harmonised Indices of Consumer Prices

Data Set: Working Paper bde9915

locate.outliers

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

locate.outliers.loops

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

plot.tsoutliers

Display Outlier Effects Detected by tsoutliers

tsoutliers-package

Automatic Detection of Outliers in Time Series

Product of the Polynomials in an ARIMA Model