Katerina Hlavackova-Schindler

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• Dr. Privatdoz.
• Senior Scientist at University of Vienna

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connec...

Accurate model selection is essential in predictive modelling across various domains, significantly impacting decision-making and resource allocation. Despite extensive research, the model selection process remains challenging. This work aims to integrate the Minimum Description Length principle with the Multi-Criteria Decision Analysis to enhance...

In this work, we present HMMLVis, an original visualization tool for multivariate Granger causal inference. More precisely, for heterogeneous Granger causality to infer causal relationships in time-series following an exponential distribution. HMMLVis is easy to use and can be applied in any scientific discipline exploring time series and their rel...

Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potentially fraudulent journal entries, each characterized by a varying number of transactions. In machine learning, heterogeneity in featu...

Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used to model various real-life phenomena: earthquakes, operations on stock markets, neuronal activity, virus propagation and many others. In this paper, we focus on MHPs with exponential decay kernels and estimate connectivity graphs, which represent the Granger causal relation...

Not much has been written about the role of triggers in the literature on causal reasoning, causal modeling, or philosophy. In this paper, we focus on describing triggers and causes in the metaphysical sense and on characterizations that differentiate them from each other. We carry out a philosophical analysis of these differences. From this, we fo...

The island of Sicily has been displaying unusual rainfall behavior and unexpected extreme precipitation events in recent decades. In this study, we investigate the Granger causal (GC) dependencies in the network of precipitation measurement sites of Sicily at different timescales (every 10 min, 1 h, 6 h, 12 h, and 24 h). We study, across seasons an...

For an efficiently managed wind farm and wind power generation under adverse weather, knowledge of meteorological parameters influencing wind speed is of crucial importance for optimized and improved forecasts. We investigate temporal effects of wind speed related processes such as wakes within the wind farm using the Heterogeneous Graphical Grange...

Using the ERA5 meteorological reanalysis data from 2000 to 2020, we investigate temporal effects of ten wind related processes in time intervals of extreme wind speed values, extracted and corrected towards wind turbine locations for a wind farm in Andau, Austria. We approach the problem by two ways, by the Granger causal inference, namely by the h...

Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used to model various real-life phenomena: earthquakes, operations on stock markets, neuronal activity, virus propagation and many others. In this paper, we focus on MHPs with exponential decay kernels and estimate connectivity graphs, which represent the Granger causal relation...

The present Special Issue of Entropy, entitled "Causal Inference for Heterogeneous Data and Information Theory", covers various aspects of causal inference. The issue presents thirteen original contributions that span various topics, namely the role of instrumental variables in causal inference, the estimation of average treatment effects and the t...

The present Special Issue of Entropy, entitled "Causal Inference for Heterogeneous Data and Information Theory", covers various aspects of causal inference. The issue presents thirteen original contributions that span various topics, namely the role of instrumental variables in causal inference, the estimation of average treatment effects and the t...

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4–6 weeks after starting current antidepressive medication. Based on the da...

Our research concerns the meteorological causes on wind speed extremes by Granger causality. We investigate 16 hourly meteorological parameters from the ERA5 database [1] and identified 62 extreme events based on a wind speed threshold in our dataset. We explore the causal effects by Granger causal inference, namely by the heterogeneous graphical G...

The number of wind farms and amount of wind power production in Europe, both on- and offshore, have increased rapidly in the past years. To ensure grid stability and on-time (re)scheduling of maintenance tasks and to mitigate fees in energy trading, accurate predictions of wind speed and wind power are needed. Particularly, accurate predictions of...

Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also performs implicit outlier detection during the clustering. AWT integrates ideas of several well-known K-Means clustering algorithms. It chooses the number of clusters autom...

Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of inter-pretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. Gait sequence of a person in the standardized motion capture fo...

Message from the Guest Editor This Special Issue focuses on causal inference models for heterogeneous data of (not only) the described heterogeneous nature. As working approaches and tools to be selected here is information theory, probability and to them related machine learning tools. Information theory here is to be interpreted broadly, includin...

The amount of wind farms and wind power production in Europe, both on- and off-shore, has increased rapidly in the past years. To ensure grid stability, on-time (re)scheduling of maintenance tasks and mitigate fees in energy trading, accurate predictions of wind speed and wind power are needed. It has become particularly important to improve wind s...

Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability of events in the future. Discovery of the underlying influence network among the dimensions of multi-dimensional temporal processes is of high importance in disciplines where...

Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability of events in the future. Discovery of the underlying influence network among the dimensions of multi-dimensional temporal processes is of high importance in disciplines where a...

Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. Gait sequence of a person in the standardized motion capture for...

Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability of events in the future. Discovery of the underlying influence network among the dimensions of multi-dimensional temporal processes is of high importance in disciplines where...

Based on the ERA5 data of hourly meteorological parameters [1], we investigate temporal effects of 12 meteorological parameters on the extreme values occurring in wind speed. We approach the problem by using the Granger causal inference, namely by the heterogeneous graphical Granger model (HGGM) [2]. In contrary to the classical Granger model propo...

Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability ofevents in the future. Discovery of the underlying influence network among the dimensions of multidimensional temporal processes is of high importance in disciplines wherea h...

The amount of wind farms and wind power production in Europe, on-shore and off-shore, increased rapidly in the past years. To ensure grid stability, omit fees in energy trading, and on-time (re)scheduling of maintenance tasks accurate predictions of wind speed and wind energy is needed. Especially for the prediction range of +48 hours up to 2 weeks...

Graphical Granger models are popular models for causal inference among time series. In this paper we focus on the Poisson graphical Granger model where the time series follow Poisson distribution. We use minimum message length principle for determination of causal connections in the model. Based on the dispersion coefficient of each time series and...

The heterogeneous graphical Granger model (HGGM) for causal inference among processes with distributions from an exponential family is efficient in scenarios when the number of time observations is much greater than the number of time series, normally by several orders of magnitude. However, in the case of “short” time series, the inference in HGGM...

Causal inference by a graphical Granger model (GGM) among p variables is typically solved by p penalized linear regression problems in time series with a given lag. In practice however, the estimates of a penalized linear regression after a finite number of steps can be still far from the optimum. Furthermore, the selection of the regularization pa...

Graphical Granger models are popular models for causal inference among time series. In this paper we focus on the Poisson graphical Granger model where the time series follow Poisson distribution. We use minimum message length principle for determination of causal connections in the model. Based on the dispersion coefficient of each time series and...

Causal inference by a graphical Granger model (GGM) among p variables is typically solved by p penalized linear regression problems in time series with a given lag. In practice however, the estimates of a penalized linear regression after a finite number of steps can be still far from the optimum. Furthermore, the selection of the regularization pa...

How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identify any kind of non-random structure or patterns in the data without restricting ourselves to a pre-de...

Discovery of temporal structures and finding causal interactions among time series have recently attracted attention of the data mining community. Among various causal notions graphical Granger causality is well-known due to its intuitive interpretation and computational simplicity. Most of the current graphical approaches are designed for homogene...

Discovery of temporal structures and finding causal interactions among time series have recently attracted attention of the data mining community. Among various causal notions graphical Granger causal-ity is well-known due to its intuitive interpretation and computational simplicity. Most of the current graphical approaches are designed for homogen...

In this paper, we propose a new method for detecting relevant variables from a priori given high-dimensional data under the assumption that input-output relation is described by a nonlinear function depending on a few variables. The method is based on the inspection of the behavior of discrepancies of a multi-penalty regularization with a component...

The special characteristics of time series data, such as their high dimensionality and complex dependencies between variables make the problem of detecting anomalies in time series very challenging. Anomalies and more precisely dependency anomalies ensue from the temporal causal dependencies. Furthermore the graphical Granger causal models provide...

The goal of our work was to apply the algorithm of (Qiu et al., 2012), using Graphical Granger causality for time series anomaly detection so that it can be used to the EEG time series of a human brain. The authors presented the algorithm in a brief form and its parametriza-tion is not sufficiently discussed. We elaborated its detailed parametrizat...

Sourav Chatterjee in 2014 proved consistency of any estimator using orthogonal least squares (OLS) together with Lasso penalty under the conditions the observations are upper bounded, with normal errors, and being independent of observations, with a zero mean and a finite variance. Reviewing his elegant proof, we come to the conclusion that the pre...

Since its introduction, transfer entropy has become a popular information-theoretic tool for detecting causal inference between two discretized random processes. By means of statistical tools we evaluate the transfer entropy of stationary processes whose continuous probability distributions are known. We study transfer entropy of processes coming f...

Since its introduction, transfer entropy has become a popular information-theoretic tool for detecting causal inference between two discretized random processes. By means of statistical tools we evaluate the transfer entropy of stationary processes whose continuous probability distributions are known. We study transfer entropy of processes coming f...

Granger causality, based on a vector autoregressive model, is one of the most popular methods for uncovering the temporal dependencies between time series. The application of Granger causality to detect inference among a large number of variables (such as genes) requires a variable selection procedure. To address the lack of informative data, so-ca...

The detection of causality in gene regulatory networks from experimental data,such as gene expression measurements, is a challenging problem. Granger causality,based on a vector autoregressive model, is one of the most popular methods for uncovering the temporal dependencies between time series, and so it can be used for estimating the causal relat...

The recovery of the causality networks with a number of variables is an important problem that arises in various scientific contexts. For detecting the causal relationships in the network with a big number of variables, the so called Graphical Lasso Granger (GLG) method was proposed. It is widely believed that the GLG-method tends to overselect cau...

In this paper we propose a new method for detecting relevant variables from a priori given high-dimensional data under the assumption that input- output dependence is described by a nonlinear function depending on a few variables. The method is based on the inspection of the behavior of discrepan- cies of a multi-penalty regularization with a compo...

The recovery of the causality networks with a number of variables is an important problem that arises in various scientific contexts. For detecting the causal relationships between variables in the network, the concept of the so called multivariate Granger causality has been proposed. Its application to the networks with a big number of variables r...

A causality graph consists of quantities of interest (nodes) and the causal (influence) relationships (edges) between them. Detection of such a graph is an important problem that arises in various scientific contexts. Multivariate Granger causality is one of the possibilities to define causal links among quantities using the time dependent observat...

Granger causality (GC), based on a vector autoregressive model, is one of the most popular methods in uncovering the temporal dependencies among time series. The original Granger model is able to detect only linear causal dependencies and many approaches were re-cently developed to extend it to the non-linear modeling. The method Copula-Granger fro...

The concept of causality is changing as human knowledge changes. Causality as an abstract notion has been traditionally studied in the field of metaphysics in philosophy. The Greek philosophers understood the time causality as explanation in general (Aristotle, 350 B.C.). The search for causes was a search for "first principles", which were meant to...

Barnett et al. in 2009 proved that Granger causality and transfer entropy causality measure are equivalent for time series which have a Gaussian distribution. Granger causality test is linear, while transfer entropy a non-linear test. Many biological and physical mechanisms show to have non-Gaussian distributions. In this paper we investigate under...

Barnett et al. in 2009 proved that Granger causality and transfer entropy causality measure are equivalent for time series which have a Gaussian distribution. Granger causality test is linear, while transfer entropy a non-linear test. Many biological and physical mechanisms show to have non-Gaussian distributions. In this paper we investigate under...

A new lower bound on minimal singular values of real matrices based on Frobe-nius norm and determinant is presented. We show that under certain assump-tions on matrix A is this estimate sharper than a recent bound from Hong and Pan based on a matrix norm and determinant.

We presented a new lower bound on minimal singular values of real matrices based on Frobenius norm and determinant and showed in [4] that under certain assumptions on the matrix is our estimate sharper than a recent lower bound from Hong and Pan [3]. In this paper we show, under which conditions is our lower bound sharper than two other recent lowe...

A new lower bound on minimal singular values of real matrices based on Frobenius norm and determinant is presented. We show that under certain assumptions on matrix A is this estimate sharper than a recent bound from Hong and Pan based on a matrix norm and determinant.

We presented a new lower bound on minimal singular values of real matrices based on Frobenius norm and determinant and showed in [4] that under certain assumptions on the matrix is our estimate sharper than a recent lower bound from Hong and Pan [3]. In this paper we show, under which conditions is our lower bound sharper than two other recent lowe...

While studying complex systems, one of the fundamental questions is to identify causal relationships (i.e., which system drives which) between relevant subsystems. In this paper, we focus on information-theoretic approaches for causality detection by means of directionality index based on mutual information estimation. We briefly review the current...

In our paper, we consider Tikhonov regularization in the reproducing Kernel Hilbert Spaces. In this space we derive upper and lower bound of the interval which contains the optimal value of Tikhonov regularization parameter with respect to the sensitivity of the solution without computing the singular values of the corresponding matrix. For the cas...

In our paper, we consider Tikhonov regularization in the reproducing Kernel Hilbert Spaces. In this space we derive upper and lower bound of the interval which contains the optimal value of Tikhonov regularization parameter with respect to the sensitivity of the solution without computing the singular values of the corresponding matrix. For the cas...

Discovering interdependencies and causal relationships is one of the most relevant challenges raised by the information era. As more and better data become available, there is an urgent need for data-driven techniques with the capability of efficiently detecting hidden interactions. As such, this important issue is receiving increasing attention in...

We focus on the recently introduced nearest neighbor based entropy estimator from Kraskov, Stögbauer and Grassberger (KSG) [10], the nearest neighbor search of which is performed by the so called box assisted algorithm [7]. We compare the performance of KSG with respect to three spatial indexing methods: box-assisted, k-D trie and projection method...

ABSTRACT We focus on the recently introduced nearest neighbor based entropy estimator from Kraskov, Stögbauer and Grassberger (KSG), the nearest neighbor search of which is performed by the so called box assisted algorithm. We compare the performance of KSG with respect to three spatial indexing methods: box-assisted, k-D trie and projection method...

Synchronization, a basic nonlinear phenomenon, is widely observed in diverse complex systems studied in physical, biological and other natural sciences, as well as in social sciences, economy and finance. While studying such complex systems, it is important not only to detect synchronized states, but also to identify causal relationships (i.e. who...

Synchronization, a basic nonlinear phenomenon, is widely observed in diverse complex systems studied in physical, biological and other natural sciences, as well as in social sciences, economy and finance. While studying such complex systems, it is important not only to detect synchronized states, but also to identify causal relationships (i.e. who...

Discovering interdependencies and causal relationships is one of the most relevant challenges raised by the information era. As more and better data become available, there is an urgent need for data-driven techniques with the capability of eciently detecting hidden interactions. As such, this important issue is receiving increasing attention in th...

Synchronization, a basic nonlinear phenomenon, is widely observed in diverse complex systems studied in physical, biological and other natural sciences, as well as in social sciences, economy and finance. While studying such complex systems, it is important not only to detect synchronized states, but also to identify causal relationships (i.e. who...

We focus on the recently introduced nearest neighbor based entropy estimator from Kraskov, Stögbauer and Grassberger (KSG) [10], the nearest neighbor search of which is performed by the so called box assisted algorithm [7]. We compare the performance of KSG with respect to three spatial indexing methods: box-assisted, k-D trie and projection method...

In this chapter a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin-constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedu...

This work extends the work in (Hlavackova-Schindler and Sanguineti 2003) investigating a class of nonlinear models by presenting their further advantages in comparison to the class of linear models. The class of nonlinear models has the structure of combinations of simple, parametrized basis functions; it includes widespread neural networks in whic...

A class of non-linear models having the structure of combinations of simple, parametrized basis functions is investigated; this class includes widespread neural networks in which the basis functions correspond to the computational units of a type of networks. Bounds on the complexity of such models are derived in terms of the number of adjustable p...

In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure...

In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure...

A class of non-linear models having the structure of combinations of simple, parametrized basis functions is investigated; this class includes widespread neural networks in which the basis functions correspond to the computational units of a type of networks. Bounds on the complexity of such models are derived in terms of the number of adjustable p...

Motivated by the requirements of the present archaeology, we are developing an automated system for archaeological classification of ceramics. The basis for classification and reconstruction of ceramics is the profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry, and can be represented by a closed...

The first aim of this project is to establish objective criteria for the definition of the form of a vessel and to create an open classification system. Secondly the main part of the classification, that is the segmentation of the profile, should be carried out on a computer aided basis. The material basis for this exemplary attempt is provided by...

The first aim of this project is to establish objective criteria for the definition of the form of a vessel and to create an open classification system. Secondly the main part of the classification, that is the segmentation of the profile, should be carried out on a computer aided basis. The material basis for this exemplary attempt is provided by...