This vignette describes how to use the **kfino** algorithm on time courses in order to detect impulse noised outliers and predict the parameter of interest.
RAJOUTER DU TEXTE
Kalman filter with impulse noised outliers (Kfino) is a robust sequential algorithm 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 latters are data points that differ significantly from other observations.
The method is described in detail in arxiv preprint https://arxiv.org/abs/2208.00961.
# Description of the `spring1` dataset
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To test the Kfino algorithm, we enclosed a real data set into the Kfino package. This data set was crated for the publication:
https://doi.org/10.1016/j.compag.2018.08.022
To trial the feasibility of using an automated weighing prototype suitable for a range of contrasting sheep farming systems, the authors automatically record the weight of 15 sheeps grazing outdoor in spring.
