Kalman Filter for Impulse Noised Outliers

A method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. '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. 'ML' (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) algorithms were implemented in 'kfino'. The method is described in full details in the following arXiv e-Print: .

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

quali

quanti

pred

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]