Proposes a method to predict both time and mark of next event in marked temporal dynamics.
problem Predicting both time and mark of the next event in marked temporal dynamics.
method Uses a mark-specific intensity function to model the dependency between time and mark.
result Outperforms state-of-the-art methods in predicting marked temporal dynamics.
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.
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.
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.
New method models MTPP without predefined intensity functions.
problem Parameterizing conditional joint PDF for MTPP.
method IFIB framework, modeling p∗(m,t) directly. result Superior experimental results on real and synthetic data.
Paper tackles reinforcement learning for asynchronous events.
problem Designing online interventions for complex asynchronous environments.
method Deep reinforcement learning of marked temporal point processes.
result Method can find effective interventions for diverse applications.
The paper models user-advertiser interactions using point processes.
problem Causal inference problems in user-advertiser interaction.
method Temporal marked point processes and neural point processes.
result Neural point processes as practical solutions.
A new framework generates high-dimensional event sequences efficiently.
problem Challenges in modeling high-dimensional marked temporal point processes.
method Conditional generator that learns from event history.
result Superior performance compared to existing methods.
This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitr…
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.
Improved Hawkes model captures price dynamics and volatility.
problem Modeling price tick structures and estimating volatility.
method Extended Hawkes model with random marks, incorporating market noise and clustering.
result Volatility formula derived and compared with realized volatility.
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.
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.
Method detects lithium-ion battery knee onset for early warning.
problem Early warning of lithium-ion battery performance changes.
method Temporal dynamics extraction and Matrix Profile anomaly detection.
result Knee onset detected for categorizing battery health.
New model detects crime linkages from text, time, and space.
problem Detecting crime linkages from limited information.
method Spatio-temporal-textual Hawkes processes with text embeddings.
result Joint modeling of space, time, and text enhances crime linkage detection.
Modeling latent dynamics in high-dimensional event sequences without prior knowledge.
problem Modeling latent dynamics in high-dimensional event sequences with unknown marker relations.
method Adversarial imitation learning framework decomposed into latent structural intensity model, efficient random walk model, and seq2seq discriminator.
result Effective detection of hidden network among markers and decent prediction for future events.
UNHaP removes noise from physiological events using Hawkes processes.
problem Challenges in identifying true events from spurious ones in physiological signal analysis.
method UNHaP uses marked Hawkes processes to distinguish and unmix true events from noise.
result UNHaP significantly reduces false detection rates and enhances event understanding.
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.
New method uses short geodesics to approximate marked length spectrum.
problem Determining a metric from its marked length spectrum.
method Recovering hypotheses from previous work using dynamical tools.
result Approximate values of MLS on a large set determine the metric.
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.
Bayesian method detects change points in time series data.
problem Detecting significant regime shifts in time series data.
method Bayesian autoregressive model with time-varying parameters.
result Enhanced estimate accuracy and forecasting power.
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.
Analyzes how venture investment strategies have evolved over time in different sectors.
problem Understanding changes in venture investment strategies across sectors over time.
method Applied PCA and TCA to analyze a dataset of 52,000 startups and 110,000 funding rounds.
result There has been a shift in venture investment towards lower-tech sectors and a rise in accelerator investments.
New stability estimate for metric rigidity in hyperbolic dynamics.
problem Metric rigidity in hyperbolic dynamics.
method Radial source estimates in Hölder-Zygmund spaces for uniformly hyperbolic dynamics.
result Metrics with same marked length spectrum are isometric in C3+ε-close metrics in any dimension ≥2. 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.
Study improves cryptocurrency price prediction using neural networks and technical indicators.
problem Improving cryptocurrency price prediction accuracy.
method Integrates technical indicators, Transformer neural network, and BiLSTM.
result Demonstrates superior performance in predicting cryptocurrency prices.
A lightweight network and NAS method improve ASC tasks.
problem Heavy computational burden in acoustic scene classification.
method Inspired by MobileNetV2, unidirectional convolutions; dynamic NAS with evolutionary algorithm.
result 90.3% F1-score on DCASE2018 task 5, 25% fewer FLOPs.
New method learns dynamic node embeddings from temporal networks.
problem Temporal networks' temporal information is often ignored or approximated.
method Continuous-time dynamic network embeddings (CTDNEs) using temporal walks.
result CTDNEs achieve an average gain of 11.9% in AUC across all methods and graphs.
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.
A novel method captures both micro- and macro-dynamics in temporal networks.
problem Capturing both micro- and macro-dynamics in temporal networks.
method Temporal Attention Point Process for micro-dynamics and a dynamics equation for macro-dynamics.
result Significantly outperforms state-of-the-arts in temporal tendency-related tasks.
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.
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.
Framework for dynamic node embeddings from graph streams.
problem Temporal prediction-based applications using graph stream data.
method ε-graph time-series representation, temporal reachability graphs, weighted temporal summary graphs.
result Dynamic embedding methods achieve better predictive performance.
Dynamic time warping applied to temporal graphs for pattern recognition.
problem Comparing temporal graphs using a proximity measure.
method Dynamic Time Warping (DTGW) on temporal graphs.
result DTGW is a flexible measure for temporal graph comparison, NP-hard in general but with polynomial-time solvable special cases.
Study periodic points on genus two surfaces, solving dynamics and geometry problems.
problem Classifying and understanding periodic points on genus two surfaces.
method Analyzing GL(2, R)-equivariant point markings and using properties of hyperelliptic involution, Weierstrass points, and golden points.
result All GL(2, R)-equivariant point markings over orbit closures arise from specific point exchanges.
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The…
DGRCL integrates dynamic and static graph relations for financial market prediction.
problem Capturing the evolving nature of stock markets while considering both temporal changes and static relational structures.
method Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, including Embedding Enhancement (EE) and Contrastive Constrained Training (CCT) modules.
result DGRCL significantly outperforms state-of-the-art TGL baselines on NASDAQ and NYSE datasets.
SNP extends Neural Processes to handle temporal dependencies in sequences.
problem Handling temporal dependencies in sequences of stochastic processes.
method Integrates a temporal state-transition model into Neural Processes.
result First 4D model capable of dynamic 3D scene modeling.
Recommender system improves with temporal representations.
problem Improving interpretability and performance in recommender systems.
method Incorporates temporal representations via recurrent point process in continuous time.
result Characterizes effects of perception, interest, and seasonal changes on reviews.
A new kernel framework analyzes spatio-temporal data from dynamic equations.
problem Analyzing spatio-temporal data from dynamic equations with noisy measurements.
method Kernel-based framework with representer theorem for minimizing error with given samples.
result Minimizes error in solutions of dynamic equations with noisy spatio-temporal data.
New training algorithm enhances SNNs for temporal signal processing.
problem Lack of robust training algorithms for large-scale SNNs.
method Formulated SNN as IIR filters, proposed training algorithm for optimal synapse filter kernels and weights.
result Model and training algorithm outperform state-of-the-art approaches in accuracy.
This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. The intensity function of the proposed process is a mixture of intensities, a…
Unified model forecasts epidemics with spatial and temporal dynamics.
problem Limited accuracy in traditional models and lack of interpretability in deep learning models.
method CSTGNN integrates Spatio-Contact SIR model with Graph Neural Networks.
result Effective spatiotemporal epidemic forecasting with interpretability.
New method proves length spectrum rigidity in various geometric settings.
problem Length spectrum rigidity in geometric settings.
method Combination of dynamical systems and geometric group theory.
result Provides concise proofs and extends classical results.
GoT-WAVE improves temporal network alignment by 25% accuracy and 64% speed.
problem Finding conserved network regions in temporal networks.
method Using graphlet-orbit transitions (GoTs) as a dynamic node similarity measure within DynaWAVE.
result GoT-WAVE outperforms DynaWAVE in accuracy and speed on synthetic networks.
Study analyzes cryptocurrency pump-and-dump dynamics using minute-level data.
problem Identifying and quantifying insider trading in cryptocurrency markets.
method Algorithmic identification of insider volume spikes, conservative profit bounds calculation, social-media verification.
result Median returns above 100%, upper-quartile returns exceeding 2000% for insider profits.
We explore martingale and convex duality techniques to study optimal investment strategies that maximize expected risk-averse utility from consumption and terminal wealth. We consider a market model with jumps driven by (multivariate) marked point processes and so-called non-linear wealth dynamics which allows to take …
DDP models dynamic comorbidity networks from event data.
problem Understanding complex temporal patterns of co-occurring diseases.
method Developed deep diffusion processes (DDP) to model dynamic comorbidity networks.
result DDP enables accurate risk prediction and interpretable disease trajectories.