Robust Time Series Filters
Weighted Repeated Median Smoothing
Bias correction factors for the robust scale estimators MAD, Sn, Qn, a...
A Robust Adaptive Online Repeated Median Filter for Univariate Time Se...
Deepest Regression (DR) filter
Robust Double Window Filtering Methods for Univariate Time Series
Robust Hybrid Filtering Methods for Univariate Time Series
Correction factors for the scale estimation of the filtering procedure...
Least Median of Squares (LMS) filter
Least Quartile Difference filter
Least Trimmed Squares (LTS) filter
A multivariate adaptive online repeated median filter
Median (MED) filter
MSCARM (Multivariate Slope Comparing Adaptive Repeated Median)
Repeated Median (RM) filter
tools:::Rd_package_title("robfilter")
Robust Regression Filters for Univariate Time Series
Robust Filtering Methods for Univariate Time Series
SCARM (Slope Comparing Adaptive Repeated Median)
Weighted Repeated Median Filters for Univariate Time Series
Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> and the slope comparing adaptive repeated median by Borowski and Fried (2013) <doi:10.1007/s11222-013-9391-7> choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393> for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.