Search Results for author:
Found 22 papers, 7 papers with code
Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting
This paper introduces the $\sigma$-Cell, a novel Recurrent Neural Network (RNN) architecture for financial volatility modeling.
Stretched and measured neural predictions of complex network dynamics
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.
Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion
Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions.
Topic Community Based Temporal Expertise for Question Routing
Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions.
Volatility-inspired $σ$-LSTM cell
Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings.
Can LSTM outperform volatility-econometric models?
Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation.
Information dynamics of price and liquidity around the 2017 Bitcoin markets crash
We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018.
Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data
Using this data, we built several deep learning architectures that utilized different combinations of the gathered information.
Implicit energy regularization of neural ordinary-differential-equation control
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.
On the impact of publicly available news and information transfer to financial markets
We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets.
Neural Ordinary Differential Equation Control of Dynamics on Graphs
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).
Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets
We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets.
Time-varying volatility in Bitcoin market and information flow at minute-level frequency
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.
Exploring Interpretable LSTM Neural Networks over Multi-Variable 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 in multi-variable time series and distinguish the contribution of variables to the prediction.
Low-dimensional statistical manifold embedding of directed graphs
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.
Sensing Social Media Signals for Cryptocurrency News
The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors.
Inferring short-term volatility indicators from Bitcoin blockchain
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.
Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
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.
Generalized Network Dismantling
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
Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems.
Underestimated cost of targeted attacks on complex networks
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.
A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering
A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering.
