Nino Antulov-Fantulin

ETH Zurich | ETH Zürich

This paper introduces the $\sigma$-Cell, a novel Recurrent Neural Network (RNN) architecture for financial volatility modeling. Bridging traditional econometric approaches like GARCH with deep learning, the $\sigma$-Cell incorporates stochastic layers and time-varying parameters to capture dynamic volatility patterns. Our model serves as a generati...

Question Routing (QR) in Community-based Question Answering (CQA) websites aims at recommending newly posted questions to potential users who are most likely to provide “accepted answers”. Most of the existing approaches predict users’ expertise based on their past question answering behavior and the content of new questions. However, these approac...

Question Routing in Community-based Question Answering websites aims at recommending newly posted questions to potential users who are most likely to provide "accepted answers". Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer...

Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings. The recent advancements in neural networks (NN) in the deep learning field have naturally offered novel econometric modeling tools. However, there is still a lack of explainability and stylized know...

We study information dynamics between the largest Bitcoin exchange markets during the bubble in 2017–2018. By analyzing high-frequency market microstructure observables with different information-theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study time-varying components...

We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous-time nonlinear dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we present a neural ODE control (NODEC) framework and find that it can learn feedback control signals...

Background Forecasting new cases, hospitalizations, and disease-induced deaths is an important part of infectious disease surveillance and helps guide health officials in implementing effective countermeasures. For disease surveillance in the US, the Centers for Disease Control and Prevention (CDC) combine more than 65 individual forecasts of these...

Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation. However, although many novel deep learning models were recently proposed, they still have a "hard time" surpassing strong econometric volatility models. Why is this the case? The volatility prediction task is...

The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems, both control energy and cost constraints play a significant role. Although such optimal control problems can be formulated within the framework of variational calculus, their solution for complex systems...

Epidemic models often reflect characteristic features of infectious spreading processes by coupled nonlinear differential equations considering different states of health (such as susceptible, infectious or recovered). This compartmental modelling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochas...

We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with different information theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study the time-varying com...

Understanding the variations in trading price (volatility), and its response to exogenous information, is a well-researched topic in finance. In this study, we focus on finding stable and accurate volatility predictors for a relatively new asset class of cryptocurrencies, in particular Bitcoin, using deep learning representations of public social m...

We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sourc...

We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a non-profit organization that crawls a large part of the web. We develop a processing pipeline to identify news articles...

The current global systemic crisis reveals how globalised societies are unprepared to face a pandemic. Beyond the dramatic loss of human life, the COVID-19 pandemic has triggered widespread disturbances in health, social, economic, environmental and governance systems in many countries across the world. Resilience describes the capacities of natura...

Forecasting new cases, hospitalizations, and disease-induced deaths is an important part of infectious disease surveillance and helps guide health officials in implementing effective countermeasures. For disease surveillance in the U.S., the Centers for Disease Control and Prevention (CDC) combine more than 65 individual forecasts of these numbers...

In this article, we analyze the time series of minute price returns on the Bitcoin market through the statistical models of the generalized autoregressive conditional heteroscedasticity (GARCH) family. We combine an approach that uses historical values of returns and their volatilities—GARCH family of models, with a so-called Mixture of Distributio...

Epidemic models often reflect characteristic features of infectious spreading processes by coupled non-linear differential equations considering different states of health (such as Susceptible, Infected, or Recovered). This compartmental modeling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochast...

Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable. In this Letter we present a versatile neural ordinary-differential-equation control (NODEC) framework with implicit energy regularization and...

This policy brief aims to promote a holistic mindset about the COVID-19 pandemic by 1) applying a complexity lens to understand its drivers, nature, and impact, 2) proposing actions to build resilient societies to pandemics, and 3) deriving principles to govern complex systemic crises. Building resilience to prevent, react to, and recover from syst...

The current COVID-19 pandemic potentially threatens the foundation of societies worldwide. Con-temporary globalisation has brought many benefits but has also increased the risk that hazards arising in one part of the global system will more readily spread to other parts. Societal resilience to COVID-19 refers to the effectiveness of the public heal...

We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a nonprofit organization that crawls a large part of the web. We develop a processing pipeline to identify news articles a...

Waiting times between two consecutive infection and recovery events in spreading processes are often assumed to be exponentially distributed, which results in Markovian (i.e., memoryless) continuous spreading dynamics. However, this is not taking into account memory (correlation) effects and discrete interactions that have been identified as releva...

We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states....

We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble model, capable of adaptively exploiting, for the forecasting, differ...

In this paper, we analyze the time-series of minute price returns on the Bitcoin market through the statistical models of generalized autoregressive conditional heteroskedasticity (GARCH) family. Several mathematical models have been proposed in finance, to model the dynamics of price returns, each of them introducing a different perspective on the...

Waiting times between two consecutive infection and recovery events in spreading processes are often assumed to be exponentially distributed, which results in Markovian (i.e., memoryless) continuous spreading dynamics. However, this is not taking into account memory (correlation) effects and discrete interactions that have been identified as releva...

We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable space. Furthermore, we analyze the connection between the geometrical properties of su...

Finding a set of nodes in a network, whose removal fragments the network below some target size at minimal cost is called network dismantling problem and it belongs to the NP-hard computational class. In this paper, we explore the (generalized) network dismantling problem by exploring the spectral approximation with the variant of the power-iterati...

Finding a set of nodes in a network, whose removal fragments the network below some target size at minimal cost is called network dismantling problem and it belongs to the NP-hard computational class. In this paper, we explore the (generalized) network dismantling problem by exploring the spectral approximation with the variant of the power-iterati...

For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics i...

We propose a novel statistical node embedding of directed graphs, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable real n-dimensional space. Furthermore, we analyze the connection to the geometrical properties of s...

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track popular news in real-time, we (i) match news from the web with tweets from...

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with...

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how thes...

In this paper we estimate the magnitude of peer and external influence on users of an opinion poll which was conducted through a Facebook application. Poll question was related to the referendum on the definition of marriage in Croatia held on \(1^{st}\) of December 2013. Through the application we collected Facebook friendship relationships, demog...

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how thes...

Supporting Information

We present a framework to simulate SIR processes on networks using weighted shortest paths. Our framework maps the SIR dynamics to weights assigned to the edges of the network, which can be done for Markovian and non-Markovian processes alike. The weights represent the propagation time between the adjacent nodes for a particular realization. We sim...

Bitcoin is the first decentralized digital cryptocurrency, which has showed significant market capitalization growth in last few years. It is important to understand what drives the fluctuations of the Bitcoin exchange price and to what extent they are predictable. In this paper, we study the ability to make short-term prediction of the exchange pr...

The robustness of complex networks under targeted attacks is deeply connected to the resilience of complex systems, i.e., the ability to make appropriate responses to the attacks. In this article, we investigated the state-of-the-art targeted node attack algorithms and demonstrate that they become very inefficient when the cost of the attack is tak...

Finding the set of nodes, which removed or (de)activated can stop the spread of (dis)information, contain an epidemic or disrupt the functioning of a corrupt/criminal organization is still one of the key challenges in network science. In this paper, we introduce the generalized network dismantling problem, which aims to find the set of nodes that,...

Recommender systems problems witness a growing interest for finding better learning algorithms for personalized information. Matrix factorization that estimates the user liking for an item by taking an inner product on the latent features of users and item have been widely studied owing to its better accuracy and scalability. However, it is possibl...

A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF with explicit orthogonality and derive general kernel-based orthogonal multiplicative update rules to...

In this paper, we propose a mapping of spreading dynamics to weighted networks, where weights represent interaction time delays on edges. With this mapping, we are able to estimate both the process evolution in time and the final outcome of a process. In a limit of process time, we establish the connection of our mapping with the bond percolation a...

Opinion polls mediated through a social network can give us, in addition to usual demographics data like age, gender and geographic location, a friendship structure between voters and the temporal dynamics of their activity during the voting process. Using a Facebook application we collected friendship relationships, demographics and votes of over...

Detection of patient-zero can give new insights to the epidemiologists about the nature of first transmissions into a population. In this paper, we study the statistical inference problem of detecting the source of epidemics from a snapshot of spreading on an arbitrary network structure. By using exact analytic calculations and Monte Carlo estimato...

The detection of an epidemic source or the patient zero is an important practical problem that can help in developing the epidemic control strategies. In this paper, we study the statistical inference problem of detecting the source of epidemics from a snapshot of a contagion spreading process at some time on an arbitrary network structure. By usin...

Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiven...

We propose two efficient epidemic spreading algorithms (Naive SIR and FastSIR) for arbitrary network structures, based on the SIR (susceptible–infected–recovered) compartment model. The Naive SIR algorithm models full epidemic dynamics of the well-known SIR model and uses data structures efficiently to reduce running time. The FastSIR algorithm is...

In this paper we introduce a statistical inference framework for estimating the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on a maximum likelihood estimation of a partial epidemic realization and involves large scale simulation of contagion spreading pro cesses fr...

The epidemic spreading on arbitrary complex networks is studied in SIR (Susceptible Infected Recovered) compartment model. We propose our implementation of a Naive SIR algorithm for epidemic simulation spreading on networks that uses data structures efficiently to reduce running time. The Naive SIR algorithm models full epidemic dynamics and can be...

Personalized recommender systems rely on personal usage data of each user in the system. However, privacy policies protecting users' rights prevent this data of being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model (MBRW) based on real clickstream graphs, as a generator of synthetic clic...

In the study of disease spreading on empirical complex networks in SIR model, initially infected nodes can be ranked according to some measure of their epidemic impact. The highest ranked nodes, also referred to as "superspreaders", are associated to dominant epidemic risks and therefore deserve special attention. In simulations on studied empirica...

In this paper we describe the system for real-time machine vision recognition of chess table and figures. Input data are two synchronized video sequences from a top-view and side-view camera showing the game of chess between two players. The top-view is used mainly for determining the positions of the figures on the table while side-view enables co...

Disease spreading on complex networks is studied in SIR model. Simulations on empirical complex networks reveal two specific regimes of disease spreading: local containment and epidemic outbreak. The variables measuring the extent of disease spreading are in general characterized by a bimodal probability distribution. Phase diagrams of disease spre...

This year's Discovery Challenge was dedicated to solving of the video lecture recommendation problems, based on the data collected at VideoLectures.Net site. Challenge had two tasks: task 1 in which new-user/new-item recommendation problem was simulated, and the task 2 which was a simulation of the clickstream-based recommendation. In this overview...

The disease spreading on complex networks is studied in SIR model. Simulations on empirical complex networks reveal two specific regimes of disease spreading: local containment and epidemic outbreak. The variables measuring the extent of disease spreading are in general characterized by a bimodal probability distribution. Phase diagrams of disease...

In this paper we demonstrate how to construct some typical rec-ommender systems in RapidMiner. Our workflow template library cur-rently includes content-based, LSI content-based, user-and item-based collaborative filtering with SVD dimensionality reduction recommender systems. The library is publicly available via myExperiment collabora-tive reposi...

In this review paper, we give a brief introduction to the complex network theory, where we explain the basic math-ematics of networks, measures, metrics and the topology properties of real networks. After a brief introduction, we give an overview of the state of the art algorithms for model-ing the network structure. Modeling the network structure...