Detection of Outliers in Time Series
Calendar Effects
Product of the Polynomials in an ARIMA Model
Stage II of the Procedure: Discard Outliers
Find outliers at consecutive time points
Jarque-Bera Test for Normality
Stage I of the Procedure: Locate Outliers (Loop Around Functions)
Stage I of the Procedure: Locate Outliers (Baseline Function)
Define Outliers in a Data Frame
Create the Pattern of Different Types of Outliers
Regressor Variables for the Detection of Outliers
Test Statistics for the Significance of Outliers
Display Outlier Effects Detected by tsoutliers
Print tsoutliers object
Stage II of the Procedure: Discard Outliers
Automatic Procedure for Detection of Outliers
Automatic Detection of Outliers in Time Series
Detection of outliers in time series following the Chen and Liu (1993) <DOI:10.2307/2290724> procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.