Kalman Filter for Impulse Noised Outliers.

kfino

The kfino algorithm was developped for time courses in order to detect impulse noised outliers and predict the parameter of interest mainly for data recorded on the walk-over-weighing system described in this publication:

E.González-García et. al. (2018) A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep. vol 153: 226-238. https://doi.org/10.1016/j.compag.2018.08.022

Kalman filter with impulse noised outliers (kfino) is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations.

The method is described in full details in the following arxiv preprint: https://arxiv.org/abs/2208.00961.

Installation

To install the kfino package, the easiest is to install it directly from GitLab. Open an R session and run the following commands:

if (!require("remotes")) {
  install.packages("remotes")
}
remotes::install_gitlab("isabelle.sanchez/kfino",host = "forgemia.inra.fr",
                        build_vignettes=TRUE)

Usage

Once the package is installed on your computer, it can be loaded into a R session:

library(kfino)
help(package="kfino")

Please, have a look to the vignettes that explain how to use the algorithm. The main specifications are:

• filtering data with a large number of outliers
• predicting the analyzed variable
• providing useful graphics to interpret the data

Citation

As a lot of time and effort were spent in creating the kfino algorithm, please cite it when using it for data analysis:

https://arxiv.org/abs/2208.00961.

See also citation() for citing R itself.

References

The kfino logo was created using the hexSticker package:

• Guangchuang Yu (2020). hexSticker: Create Hexagon Sticker in R. R package version 0.4.9. https://CRAN.R-project.org/package=hexSticker

Walk-over-weighing system:

• E.González-García et. al. (2018) A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep. vol 153: 226-238. https://doi.org/10.1016/j.compag.2018.08.022
• González García, Eliel, 2021, Individual liveweight of Mérinos d'Arles ewelambs, measured with a Walk-over-Weighing (WoW) system under Mediterranean grazing conditions, https://doi.org/10.15454/IXSHF7, Recherche Data Gouv, V5, UNF:6:q4HEDt0n8nzxYRxc+9KK8g==[fileUNF]