Search Results for author: Peter Filzmoser

Found 7 papers, 3 papers with code

Efficient Computation of Sparse and Robust Maximum Association Estimators

1 code implementation29 Nov 2023 Pia Pfeiffer, Andreas Alfons, Peter Filzmoser

This paper investigates how such procedures can be used for robust sparse association estimators.

Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast

1 code implementation12 Jun 2023 Lukas Neubauer, Peter Filzmoser

A common forecasting setting in real world applications considers a set of possibly heterogeneous time series of the same domain.

Dynamic Time Warping Time Series

Predictive change point detection for heterogeneous data

no code implementations11 May 2023 Anna-Christina Glock, Florian Sobieczky, Johannes Fürnkranz, Peter Filzmoser, Martin Jech

A change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced and characterised in relation to other state-of-the-art online CPD routines which it outperforms in terms of false positive rate and out-of-control average run length.

Change Point Detection

Multivariate outlier explanations using Shapley values and Mahalanobis distances

no code implementations18 Oct 2022 Marcus Mayrhofer, Peter Filzmoser

For the purpose of explaining multivariate outlyingness, it is shown that the squared Mahalanobis distance of an observation can be decomposed into outlyingness contributions originating from single variables.

Outlier Interpretation Position

Extending compositional data analysis from a graph signal processing perspective

no code implementations25 Jan 2022 Christopher Rieser, Peter Filzmoser

Traditional methods for the analysis of compositional data consider the log-ratios between all different pairs of variables with equal weight, typically in the form of aggregated contributions.

Variable selection with genetic algorithms using repeated cross-validation of PLS regression models as fitness measure

2 code implementations17 Nov 2017 David Kepplinger, Peter Filzmoser, Kurt Varmuza

Due to multicollinearity, partial least squares regression is often more appropriate, but rarely considered in genetic algorithms due to the additional cost for estimating the optimal number of components.

Computation

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