Extremely accurate prediction of dynamical system bifurcations using control inputs.
problem Predicting complex bifurcation structures in dynamical systems.
method Extending extreme learning machines with control inputs to model system dynamics.
result The model can nearly reproduce the entire structure of bifurcations using only a few parameter values.
GARCH-UGH improves VaR estimation for financial risk management.
problem Dynamic estimation of extreme VaR in financial time series.
method AR-GARCH filtering followed by a bias-reduced extreme value estimator.
result GARCH-UGH estimates are more accurate than conventional methods.
Anomaly-aware forecast improves accuracy for extreme events.
problem Challenges in automatically detecting and learning from extreme events and anomalies in large-scale datasets.
method Proposes an anomaly-aware forecast framework that automatically detects and incorporates anomalies using an attention mechanism and dynamic uncertainty optimization.
result Demonstrated superior accuracy and reduced uncertainty on three datasets with different types of anomalies.
Deep learning models learn chaotic system dynamics from real and simulated data.
problem Training deep learning models for chaotic systems requires big data.
method Jointly train deep neural networks on real and simulated data, enforcing physical laws.
result Proposes knowledge-based deep learning (KDL) for accurate forecasting of chaotic systems.
The paper proves properties of strata of differentials, showing they are affine and extremal.
problem Properties of strata of differentials, particularly their geometry and tautological rings.
method Analyzing the tautological rings and using Teichmüller dynamics to prove properties.
result Strata of differentials are affine and their stratification is extremal.
ML models predict extreme events in the Hénon map with accuracy scaling with system parameters.
problem Predicting extreme events in chaotic dynamical systems like the Hénon map.
method Used machine learning algorithms to analyze and forecast extreme events in the Hénon map.
result The success rate of ML models depends on prediction time, number of training samples, and network size, with scaling relations to the system's topological entropy.
XR improves search advertising relevance predictions.
problem Accurately predicting relevance of millions of search labels.
method eXtreme Regression (XR) with new evaluation metrics and XReg algorithm.
result XReg outperforms state-of-the-art methods by up to 50% in XR error metric.
We here present a model of the dynamics of extremism based on opinion dynamics in order to understand the circumstances which favour its emergence and development in large fractions of the general public. Our model is based on the bounded confidence hypothesis and on the evolution of initially anti-conformist agents to…
This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.
problem Understanding dynamic connectedness in global supply chain infrastructure portfolios under various risk factors and extreme events.
method Time-varying parameter vector autoregression (TVP-VAR) model to study spillover and interconnectedness of risk factors.
result Risk shocks influence dynamic connectedness between portfolios and risk factors, and extreme events affect investment outcomes.
Modeling time-varying extreme value dependence in European stock markets.
problem Non-stationary extremal dependence between European stock markets.
method Regression model for angular density of bivariate extreme value distribution.
result Evidence of increasing extremal dependence in recent years.
Study tail risk in high-frequency finance using L1-regularized regression.
problem Measuring tail risk dynamics in high-frequency financial markets.
method Dynamic extreme value regression model with L1-regularized maximum likelihood estimator. result Severity of extreme losses well predicted by low price impact in high volatility periods.
PASTIS selects minimal models from stochastic dynamics data.
problem Overfitting in model selection for stochastic dynamics.
method Combining likelihood-estimation statistics with extreme value theory.
result PASTIS reliably identifies minimal models, even with low sampling rates or error.
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
Develops a method to estimate extreme event statistics in high-dimensional systems with few samples.
problem Estimating extreme event statistics in high-dimensional nonlinear systems with limited data.
method Sequential sampling strategy using Gaussian process regression and Bayesian inference.
result Accurately estimates extreme event statistics in a high-dimensional system with limited samples.
Study shows similarities and differences in crypto and equity dynamics during pandemic.
problem Comparing cryptocurrency and equity market dynamics during the pandemic.
method New methodologies applied to study cryptocurrency and equity market dynamics, including recently introduced methods for trajectory and anomaly analysis.
result Cryptocurrencies exhibit stronger collective dynamics and correlation, while equities show greater persistence in anomalies over time.
Extends extreme value mixture models to identify changepoints in financial extreme regimes.
problem Inference over financial extreme regimes is affected by threshold choice.
method Extends extreme value mixture models to account for distributional extreme changepoints using MCMC algorithms.
result Inclusion of different extreme regimes improves financial applications compared to static and dynamic approaches.
Study examines extreme and erratic cryptocurrency behaviour during COVID-19.
problem Analyse extreme and erratic cryptocurrency behaviour during the pandemic.
method Analyze distribution extremities and structural breaks in 51 cryptocurrencies.
result Identify cryptocurrencies with most irregular extreme and erratic behaviour.
Framework reconstructs missing spatio-temporal data for extreme value prediction.
problem Predicting extreme values from incomplete spatio-temporal data.
method Convolutional deep neural networks and autoencoder-like models for conditional sampling.
result Framework produces accurate reconstructions of missing data for extremal values.
New risk models use chaotic attractors to predict extreme events.
problem Predicting Black Swan events in financial markets.
method Combining heavy-tailed priors with chaotic dynamics (Lorenz and Rossler systems).
result Models generate volatility clustering, fat tails, and extreme events.
PhysicsFormer improves TSF models for GSWF with WEATHER-5K dataset.
problem Lack of comprehensive datasets for GSWF.
method PhysicsFormer combines dynamic core and Transformer, enforcing physical consistency.
result PhysicsFormer outperforms TSF models in operational forecasting.
Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.
problem Static label ordering in multi-label classification limits model performance.
method Combining dynamic classifier chains with XGBoost for efficient multi-label prediction.
result Dynamic label ordering improves model performance and reduces training costs.
Quantum algorithm for dynamic asset allocation using expected shortfall.
problem Dynamic risk management in finance, especially tail risks.
method Quantum annealing algorithm in QUBO form for expected shortfall constraint.
result Quantum algorithm provides a faster solution for dynamic asset allocation.
A new method reduces uncertainty in predicting rare extreme events without assuming their presence in training data.
problem Predicting rare and extreme events in complex systems with high uncertainty.
method Extreme Event Aware (e2a or η) learning, which enforces extreme event statistics during training.
result Models generate unprecedented extreme events even when training data lacks extremes.
Polynomial processes model energy prices with rich dynamics.
problem Modeling extreme energy price behavior.
method Developed one- and two-factor models using polynomial processes.
result Polynomial processes generate rich dynamics suitable for extreme price behavior.
Mass inequality for black ring spacetimes, showing extreme solutions.
problem Establishing mass-angular momentum relation for black ring spacetimes.
method Mathematical derivation of mass-angular momentum inequality.
result The inequality is saturated for extreme Pomeransky-Sen'kov black ring solutions.
The paper tackles catastrophic risk in reinforcement learning using extreme value theory.
problem Mitigating catastrophic risk in sequential decision making with limited observations.
method Developed POTPG, a policy gradient algorithm based on extreme value theory.
result POTPG outperforms common benchmarks in numerical experiments.
Cryptocurrency markets exhibit violent, synchronised drawdowns, challenging diversification claims.
problem Cryptocurrency markets' violent drawdowns challenge diversification claims.
method Dynamic conditional tail dependence analysis
result Near-complete and stable lower-tail graph, upper tail that thins over time, dissolution of token categories into a core.
Novel path planning improves UAV detection of extreme anomalies.
problem Improper criteria for UAV anomaly detection.
method Mathematical criteria guiding UAV towards strong anomalies.
result Proposed approach outperforms traditional methods in various applications.
The study identifies and analyzes different market regimes in equity markets using advanced signal processing techniques.
problem Understanding and quantifying the dynamics of different market regimes in equity markets.
method Data-driven Hilbert--Huang Transform for regime identification, Holo--Hilbert Spectral Analysis for profiling, and Variable-Length Markov Chains for return dynamics modeling.
result Developed markets normalize more effectively as stress subsides, while developing markets retain residual tail dependence and downside persistence.
The paper calculates extreme measures in continuous time conic finance.
problem Determining valuation bounds for financial claims.
method Using dynamic spectral risk measures and estimating extreme measures from market data.
result Explicit formulas for extreme measures' Radon-Nykodim derivatives and estimation methods.
New model explains volatility after extreme stock market events.
problem Understanding volatility dynamics after extreme stock market events.
method Proposed a new dynamical model using high frequency minute data.
result Volatility after extreme events follows a stretched exponential decay initially and a power law decay later.
Improved forecasting of financial risk using Diffusion-Copula framework.
problem Capturing complex, asymmetric dependence structures in financial markets.
method Explicitly decouples marginal distribution learning from dependence structure using Mixture Density Networks and Classification-Diffusion Copula.
result Superior performance in forecasting systemic extremes of marginal and joint events.
Proposes models for dynamic tail inference in heavy-tailed time series.
problem Predicting time-varying extreme event probabilities in heavy-tailed and nonlinear time series.
method White noise process with conditionally log-Laplace stochastic volatility, conditional Pareto-tailed, with tail exponent from log-volatility's mean absolute innovation.
result Effective estimation of dynamically changing extreme event probabilities with a simple modeling method.
We present a simplified model for the exploitation of finite resources by interacting agents, where each agent receives a random fraction of the available resources. An extremal dynamics ensures that the poorest agent has a chance to change its economic welfare. After a long transient, the system self-organizes into a …
PBC improves AI and dynamical subseasonal forecasts by reducing biases.
problem Subseasonal forecast accuracy drops due to model biases and compounding errors.
method Probabilistic bias correction (PBC) using machine learning to correct historical forecasts.
result PBC doubles AI Forecasting System's subseasonal skill and improves dynamical model skill.
Empirical study on UEEs reveals liquidity's role and universal recovery patterns.
problem Understanding and stabilizing financial markets affected by UEEs.
method Comparative analysis of UEEs over different years in US stock market.
result Liquidity is dominant in UEEs emergence and recovery patterns are universal.
Model market shows self-organized behavior with price adjustments.
problem Understanding collective behavior in market economics.
method Simple model of market economics with extremal dynamics.
result Market self-organizes through price adjustments in critical states.
The paper suggests asset prices follow physical laws, allowing for accurate price movement forecasts.
problem Predicting extreme price movements in financial markets.
method Modeling asset price dynamics as a harmonic oscillator and applying the principle of stationary action.
result The theory can make accurate forecasts of price movements during market crashes and specific price displacements at other times.
ELM combines machine learning and feature engineering for anomalous diffusion detection.
problem Quantitative characterization of anomalous diffusion from single trajectories.
method Extreme Learning Machine (ELM) combined with feature engineering.
result ELM achieves satisfactory performance in AnDi challenge tasks.
This paper focuses on the interplay between the intersection theory and the Teichmueller dynamics on the moduli space of curves. As applications, we study the cycle class of strata of the Hodge bundle, present an algebraic method to calculate the class of the divisor parameterizing abelian differentials with a non-simp…
Machine learning predicts extreme events from spectral data.
problem Predicting extreme events in nonlinear systems from limited data.
method Trained a neural network to correlate spectral and temporal properties of optical fibre modulation instability.
result Predicted temporal probability distribution from high-dynamic range spectral data.
Research predicts XRP price anomalies using graph topologies.
problem Forecasting extreme price movements in XRP cryptoasset.
method Analyzed topological features of XRP transaction graphs.
result Topological features indicate extreme price surges.
Study new volume invariant in Hamiltonian dynamics.
problem Volume behavior in Hamiltonian dynamics.
method Introduce new invariant and study its properties.
result Generalize Riemannian volume expansion to sub-Riemannian manifolds.
This research explains how extreme events can emerge from adaptive systems.
problem Extreme events in complex systems.
method Examines self-similar bursts of activity in adaptive systems.
result Locally minimising fluctuations can increase system sensitivity to unpredictable perturbations.
On a symplectic manifold a family of generalized Poisson brackets associated with powers of the symplectic form is studied. The extreme cases are related to the Hamiltonian and Liouville dynamics. It is shown that the Dirac brackets can be obtained in a similar way.
This work's purpose is to understand the dynamics of limit order books in order-driven markets. We try to illustrate a dynamical trading mechanism attached to the microstructure of limit order markets. We capture the iterative nature of trading processes, which is critical in the dynamics of bid-ask pairs and the switc…
The paper introduces a new model to improve exotic option pricing.
problem Challenges in pricing exotic options and structured products due to market phenomena.
method Introduces a Diffusion-Conditional Probability Model (DDPM) with a composite loss function and P-Q dynamic game framework.
result The DDPM outperforms traditional models in dynamic games for European and Asian options, but underestimates tail risks.
The majority of real-world networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook, ...). To understand the structural behavior of nodes in these large dynamic networks, it may be necessary to model the dynamics of behavioral roles representing the main connectivity patterns over time. In th…