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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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176353529705 · Jun 202019922001200920182026
48 results for hybrid marked point processes

Introduces a new class of hybrid processes combining Markov chains and Hawkes processes.

problem Characterize and ensure existence and uniqueness of complex hybrid marked point processes.
method Defines hybrid marked point processes implicitly via intensity and state process interactions, proving existence and uniqueness under general assumptions.
result Proves existence and uniqueness of hybrid marked point processes, extending existing results.

Modeling time series with jumps using neural networks and stochastic processes.

problem Capturing the dynamics of time series with both continuous flows and discrete jumps.
method Introducing Neural Jump Stochastic Differential Equations (Neural JSDEs) that extend Neural Ordinary Differential Equations (Neural ODEs) with a stochastic process term.
result Demonstrated the model's predictive capabilities on various datasets, including Hawkes processes, Stack Overflow awards, medical records, and earthquake monitoring.

Develops quasi-likelihood analysis for marked point processes and applies it to Hawkes processes.

problem Analyzing multivariate marked point processes and their applications.
method Quasi-likelihood analysis for a general class of multivariate marked point processes, with focus on marked Hawkes processes.
result The quasi-likelihood analysis for marked Hawkes processes provides explicit conditions for ergodicity and Markovian transformation.

The paper extends intensity models for limit order books using marked point processes.

problem Modeling intensity ratios in limit order books with state dependency and clustering.
method Developed a new model combining three multiplicative components for marked point processes.
result The new model outperforms other intensity-based methods in predicting market order signs and aggressiveness.

This paper tackles efficient learning for factorial marked temporal point processes.

problem Efficient learning for factorial marked temporal point processes.
method Decoupled learning method with two procedures: ADM-M and Fast ISTA, and a reformulated Logistic Regression model.
result Empirical results show the efficiency of the decoupled and reformulated method.

Paper introduces a neural network-based non-stationary influence kernel for complex event data.

problem Modeling complex, non-stationary, and dependent discrete event data.
method Neural Spectral Marked Point Processes (NSMPP) with a versatile non-stationary influence kernel.
result NSMPP outperforms state-of-the-art models on synthetic and real data.

Neural marked point processes show saturation with complexity, leading to new simple architectures.

problem Performance saturation in neural marked point processes with complex architectures.
method Proposed GCHP with graph convolutional layers and likelihood ratio loss.
result GCHP reduces training time and improves model performance.

New model captures time and mark inter-dependence in TPPs.

problem Limited predictive performance of conditionally independent TPP models on entangled time and mark interactions.
method Developed a multivariate TPP that models conditional inter-dependence of time and mark, using both intensity-based and intensity-free models.
result Proposed TPP models outperform conditionally independent and dependent models in standard prediction tasks.

Framework handles both exchangeable and non-exchangeable event sequences without tuning.

problem Handling both exchangeable and non-exchangeable event sequences efficiently.
method Parametric Hawkes-process-inspired conditional probability mass function with variational inference.
result Competitive computational and predictive performance against state-of-the-art methods.

Predict cell loads in cellular networks using statistical learning of geometric marks.

problem Predicting cell loads in cellular networks using geometric marks.
method Statistical regression model and scattering moments of random measures.
result Scattering moments can capture similar geometry information as baseline approach and improve performance.

A new method for pricing derivatives using self-exciting dynamics and finite-difference transforms.

problem Pricing derivatives with accumulated marks using a self-exciting marked point process.
method Derive discounted pricing equation as a PIDE, transform to one-dimensional PIDEs, use Laplace/Fourier transform, approximate jump term, solve using finite difference scheme.
result Efficiently price derivatives with accumulated marks using a novel finite-difference and transform approach.

A new method uses Transformers for efficient prediction of marked point processes.

problem Efficiently predicting the next event in a sequence given its history.
method Modeling conditional inter-event times with a mixture of log-normals and marks with a Transformer architecture.
result The method achieves state-of-the-art performance and is faster during inference.

Proposes a new framework to disentangle event influences in MTPP.

problem Underexplored how individual events influence overall dynamics over time.
method Decoupled MTPP framework using Neural Ordinary Differential Equations (Neural ODEs).
result Significantly improves performance on real-life datasets compared to state-of-the-art methods.

Methodology for estimating marked Hawkes processes with neural networks.

problem Estimating conditional intensity of marked Hawkes processes.
method Proposes two models: Shallow Neural Hawkes with marks and Neural Network for Non-Linear Hawkes with Marks.
result Validation on synthetic datasets and real-world cryptocurrency order book data.

This paper models how features influence event triggers in high-dimensional networks.

problem Estimating context-dependent networks in high-dimensional marked point processes.
method Leveraging compositional time series and regularization methods, the paper considers autoregressive multinomial and logistic-normal models for network estimation.
result The logistic-normal model leads to a convex negative log-likelihood objective and captures dependence across categories.

Differentiable adversarial attacks improve model robustness in MTPP models.

problem Improving model robustness against adversarial attacks in MTPP models.
method Proposed a differentiable adversarial attack scheme PERMTPP that addresses the sequential nature and varying time-scales of MTPPs.
result Demonstrated offensive and defensive capabilities, and reduced inference times on real-world datasets.

An adversarial detector identifies anomalous sequences in sequential data.

problem Detecting anomalous sequences in one-class settings with limited data.
method Solves a minimax problem to find an optimal detector against the worst-case sequences from a generator, using marked point process model.
result Demonstrated good performance on simulations and real credit card fraud datasets.

Proves Grothendieck-Teichmüller group acts on specific mapping class groups.

problem Proving the action of GT^\widehat{GT} on specific mapping class groups.
method Analyzes the Grothendieck-Teichmüller group and mapping class groups.
result Proves GT^\widehat{GT} acts on Γ^g,0\widehatΓ_{g,0} and Γ^g,1\widehatΓ_{g,1} for all g>0g>0.

We study body-and-hinge and panel-and-hinge chains in R^d, with two marked points: one on the first body, the other on the last. For a general chain, the squared distance between the marked points gives a Morse-Bott function on a torus configuration space. Maximal configurations, when the distance between the two marke…

2008-12-07abs ↗pdf ↗

The paper tackles robust control for insurance contracts under uncertain transition rates.

problem Maximizing utility in insurance contracts with uncertain transition rates.
method Novel robust utility maximization problem under bounded cumulative transition rate uncertainty, using worst-case scenario analysis.
result Existence and uniqueness of worst-case and best-case reserves for insurance contracts.

New MTPP model offers interpretable predictions with state-of-the-art performance.

problem Inexpressive models lack interpretability, while neural models sacrifice interpretability for performance.
method Extends Hawkes process to a hypernetwork with a latent space, making it flexible and interpretable.
result Achieves state-of-the-art performance across various tasks and metrics.

Exact asymptotic solutions found for nonlinear Hawkes processes.

problem Analytical solutions for nonlinear Hawkes processes with positive and negative feedbacks.
method Field master equation approach to classify steady-state solutions.
result Explicit power law formulas for steady-state intensity distributions Pss(λ)λ1aP_{\mathrm{ss}}(λ)\propto λ^{-1-a}, with aa as a function of parameters.

The paper develops algorithms to increase social activity online.

problem Increasing user engagement in social networks.
method Modeling social activity as marked temporal point processes and deriving SDEs with jumps to develop online algorithms.
result The developed algorithms consistently steer social activity more effectively than existing methods.

Method improves simulation accuracy by mitigating distribution shift in hybrid systems.

problem Mitigating distribution shift in machine-learning augmented hybrid simulation.
method Tangent-space regularized estimator to control distribution shift.
result Marked improvements in simulation accuracy, especially for systems with high distribution shift.

Optimizes learning schedules for better memory retention.

problem Finding the best review schedule for spaced repetition.
method Flexible representation of spaced repetition using marked temporal point processes and optimal control for stochastic differential equations with jumps.
result Optimal reviewing schedule is the recall probability of content.

Survey on modeling event sequences through temporal processes.

problem Modeling phenomena with sequences of events over continuous time.
method Probabilistic models based on point processes, categorized into simple, marked, and spatio-temporal.
result Analysis of existing approaches and their applicability to prediction and modeling.

We introduce a new model for describing the fluctuations of a tick-by-tick single asset price. Our model is based on Markov renewal processes. We consider a point process associated to the timestamps of the price jumps, and marks associated to price increments. By modeling the marks with a suitable Markov chain, we can…

2013-05-01abs ↗pdf ↗

Study cohomology of surfaces with punctures and boundaries, proving bounds on rational cohomology.

problem Understanding cohomology of surfaces with punctures and boundaries.
method Two proofs showing congruence subgroups have enormous rational cohomology.
result Bounds on cohomology are super-exponential in number of punctures and boundary components.

In this work we present a new approach on studying dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have research the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generaliz…

2009-09-15abs ↗pdf ↗