no code implementations • 4 Sep 2023 • German Rodikov, Nino Antulov-Fantulin
This paper introduces the $\sigma$-Cell, a novel Recurrent Neural Network (RNN) architecture for financial volatility modeling.
no code implementations • 12 Jan 2023 • Vaiva Vasiliauskaite, Nino Antulov-Fantulin
Focusing on complex systems whose dynamics are described with a system of first-order differential equations coupled through a graph, we show that extending the model's generalizability beyond traditional statistical learning theory limits is feasible.
no code implementations • 4 Aug 2022 • Vaibhav Krishna, Nino Antulov-Fantulin
Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions.
no code implementations • 5 Jul 2022 • Vaibhav Krishna, Vaiva Vasiliauskaite, Nino Antulov-Fantulin
Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions.
no code implementations • 14 May 2022 • German Rodikov, Nino Antulov-Fantulin
Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings.
no code implementations • 23 Feb 2022 • German Rodikov, Nino Antulov-Fantulin
Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation.
no code implementations • 17 Nov 2021 • Vaiva Vasiliauskaite, Fabrizio Lillo, Nino Antulov-Fantulin
We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018.
1 code implementation • 27 Oct 2021 • M. Eren Akbiyik, Mert Erkul, Killian Kaempf, Vaiva Vasiliauskaite, Nino Antulov-Fantulin
Using this data, we built several deep learning architectures that utilized different combinations of the gathered information.
no code implementations • 11 Mar 2021 • Lucas Böttcher, Nino Antulov-Fantulin, Thomas Asikis
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.
1 code implementation • 22 Oct 2020 • Metod Jazbec, Barna Pásztor, Felix Faltings, Nino Antulov-Fantulin, Petter N. Kolm
We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets.
1 code implementation • 17 Jun 2020 • Thomas Asikis, Lucas Böttcher, Nino Antulov-Fantulin
We study the ability of neural networks to calculate feedback control signals that steer trajectories of continuous time non-linear dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs).
no code implementations • 19 May 2020 • Nino Antulov-Fantulin, Tian Guo, Fabrizio Lillo
We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets.
no code implementations • 1 Apr 2020 • Irena Barjašić, Nino Antulov-Fantulin
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.
3 code implementations • 28 May 2019 • Tian Guo, Tao Lin, Nino Antulov-Fantulin
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 in multi-variable time series and distinguish the contribution of variables to the prediction.
1 code implementation • ICLR 2020 • Thorben Funke, Tian Guo, Alen Lancic, Nino Antulov-Fantulin
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.
no code implementations • 27 Mar 2019 • Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, Nino Antulov-Fantulin
The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors.
no code implementations • 19 Sep 2018 • Nino Antulov-Fantulin, Dijana Tolic, Matija Piskorec, Zhang Ce, Irena Vodenska
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.
no code implementations • 12 Feb 2018 • Tian Guo, Albert Bifet, Nino Antulov-Fantulin
In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market.
2 code implementations • 4 Jan 2018 • Xiao-Long Ren, Niels Gleinig, Dirk Helbing, Nino Antulov-Fantulin
In this paper, we introduce the generalized network dismantling problem, which aims to find the set of nodes that, when removed from a network, results in a network fragmentation into subcritical network components at minimum cost.
Social and Information Networks Statistical Mechanics Physics and Society Computation
no code implementations • 16 Oct 2017 • Vaibhav Krishna, Tian Guo, Nino Antulov-Fantulin
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems.
no code implementations • 10 Oct 2017 • Xiao-Long Ren, Niels Gleinig, Dijana Tolic, Nino Antulov-Fantulin
Finally, for the case when it is possible to attack links, we propose a simple and efficient edge removal strategy named Hierarchical Power Iterative Normalized cut (HPI-Ncut). The results on real and artificial networks show that the HPI-Ncut algorithm outperforms all the node removal and link removal attack algorithms when the cost of the attack is taken into consideration.
1 code implementation • 29 Sep 2017 • Dijana Tolic, Nino Antulov-Fantulin, Ivica Kopriva
A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering.