Study examines HTE estimation from time-to-event data with competing events.
problem Estimating HTEs from time-to-event data with competing events.
method Outcome modeling approach using plug-in estimators for potential outcomes.
result Competing events introduce new challenges for HTE estimation.
Develops a neural model to predict event occurrence and timing.
problem Standard event time models ignore the distinction between event occurrence probability and predicted time.
method Introduces a conditional event time model using a neural network with a binary stochastic layer.
result Shows superior event occurrence and timing predictions on various datasets.
Bayesian model improves categorization of explosions from sparse data.
problem Challenges in categorizing explosions from limited data.
method Bayesian update to Event Categorization Matrix model with Bayesian Decision Theory.
result Consistent gains in overall accuracy and lower false negative rates.
Proposes a model for predicting events from event streams.
problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.
Improved neural models for diverse user event sequences.
problem Challenges in modeling diverse user event sequences.
method Mixtures of latent embeddings with amortized variational inference.
result Systematic improvements over existing work for various predictive metrics.
This paper evaluates data enrichment techniques for rare event detection in manufacturing.
problem Rare events in manufacturing lead to unplanned downtime and high energy consumption.
method Time series data augmentation, sampling, and imputation techniques combined with supervised machine learning.
result Data enrichment enhances rare failure event detection and prediction by up to 48%.
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty…
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statisti…
Paper introduces a novel point process model for graph data using GNNs.
problem Modeling discrete event data over graphs with influence kernel.
method Combines Hawkes kernel and Graph Neural Networks (GNN) for event prediction.
result Achieves superior predictive performance compared to state-of-the-art.
ProxiModel extracts high-quality news events from news corpora.
problem Mining high-quality structured event knowledge from noisy news data.
method ProxiModel uses a proximity-network to model event correlation within and across news corpora.
result ProxiModel efficiently and effectively extracts high-quality event descriptors and attributes.
New model for multivariate discrete event data with flexible interactions.
problem Modeling multivariate discrete event data with categorical interactions.
method Developed a new modeling approach with convex constraints, two estimation procedures (LS and ML).
result Proposed model can capture arbitrary shapes of historical event influence.
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.
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.
TransformerLSR models longitudinal, recurrent, and survival data jointly.
problem Joint modeling of longitudinal measurements, recurrent events, and survival data with dependencies.
method Transformer-based deep learning framework integrating deep temporal point processes and latent structure representation.
result TransformerLSR effectively models all three components simultaneously, demonstrating necessity and effectiveness through simulations and real-world data.
This paper explores neural models to improve modeling of Hawkes process intensity functions.
problem Traditional Hawkes process intensity function's parametrized kernel function biases future event predictions.
method Uses neural models to model the kernel function of Hawkes process intensity function.
result Neural models can better capture future event characteristics using past events data.
The report evaluates heuristics for learning timescale graphical event models.
problem Lack of heuristics for determining hyper-parameters in timescale graphical event models.
method Proposed and evaluated different heuristics for hyper-parameter determination and refined an existing distance measure.
result Conclusions about the applicability of different heuristics on synthetic data.
Modeling solar ramping events with spatio-temporal point processes.
problem Predicting solar ramping events influenced by weather conditions.
method Novel spatio-temporal categorical point process model.
result Effective modeling of spatio-temporal correlations in solar ramping events.
New algorithm detects unique events in time series data.
problem Detecting anomalous events in unknown properties.
method Model-free, unsupervised detection using Temporal Outlier Factor (TOF).
result TOF outperforms traditional outlier detection methods.
ForecastQA creates a new QA task for event forecasting from text data.
problem Forecasting future events from unstructured text data.
method Formulated a restricted-domain, multiple-choice QA task for event forecasting.
result Best model achieves 60.1% accuracy, lagging behind human performance by about 19%
Model compresses event-like contexts using gated surprise signals.
problem Perceiving a dynamic world as organized events.
method Hierarchical, surprise-gated recurrent neural network architecture.
result Achieves best performance on multiple event processing tasks.
Multivariate Bernoulli autoregressive (BAR) processes model time series of events in which the likelihood of current events is determined by the times and locations of past events. These processes can be used to model nonlinear dynamical systems corresponding to criminal activity, responses of patients to different med…
Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the even…
New method generates synthetic survival data by conditioning on event times and censoring indicators.
problem Generating accurate synthetic survival data with censored event times.
method Conditioning covariates on event times and censoring indicators using existing tabular data generation models.
result Our method consistently outperforms baselines and improves survival model performance.
A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The pri…
Harmoniums model multiple time-to-event variables in survival analysis.
problem Survival analysis with multiple, independently censored time-to-event variables and missing observations.
method Energy-based approach with bi-partite structure (harmoniums).
result Harmoniums capture non-linear patterns between time recordings.
We present the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with binary state representations and noisy-OR-like transitions. The distribution can be used to learn structure in discrete timeseries data by simultaneously inferring a s…
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process. In many cases, such events can be described as a collection of categorical values that are considered as entities of different types, which we call heterogeneous categor…
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models. A data set of temporal event sequences can be represented as a data set of three-dimensional points, each point is defined by three varia…
Models predict fire and other emergencies in Edmonton.
problem Accurate prediction of emergency events for timely response.
method Data collection, descriptive analysis, feature selection, and negative binomial regression.
result Models perform well, with acceptable prediction errors for weekly and monthly periods.
EvAn detects anomalies in event-based camera data with reduced complexity.
problem Anomaly detection in event-based camera data.
method Dual discriminator cGAN on event data with learned representation.
result Reduction in computational complexity compared to state-of-the-art methods.
Simple tabular event prediction model outperforms existing methods.
problem Predicting events from tabular data with historic events.
method Standard autoregressive LLM-style transformers with elementary positional embeddings and causal language modeling.
result Simple model outperforms existing approaches across various datasets and use-cases.
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
problem Predicting clinical outcomes from irregularly sampled EHR data with competing events.
method Neural ODE-based Recurrent Neural Networks (ODE-RNN) for flexible survival time estimation.
result SurvLatent ODE outperforms Khorana Risk scores for VTE risk prediction.
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…
Bayesian BIC for multi-trial data improves VAR model order selection.
problem Optimal VAR model order selection for multi-trial event-based data.
method Derive and apply Bayesian Information Criterion (BIC) for multi-trial ensemble data.
result Multi-trial BIC successfully recovers real model order and estimates small model order.
Generates synthetic health data from patient visits.
problem Lack of longitudinal, event-based medical data.
method Transformed longitudinal data into summary statistics, trained GAN.
result Synthetic data closely resembles real data univariately.
Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of d…
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventiv…
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such coho…
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as…
stCEG models spatial events using Chain Event Graphs in R.
problem Modeling events over spatial areas.
method Full specification of CEG models from data, interactive plots, web GUI.
result First software for CEGs with full model customisation.
Real-time event detection using human sensor data and adaptive machine learning.
problem Lack of effective real-time event detection using human sensor data.
method Combination of corroborative and probabilistic sources with drift adaptive machine learning.
result Automated continuous learning maintains high performance in the face of concept drift.
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …
AUC is unreliable in rare event settings but stable with moderate numbers of events.
problem Misleading performance metrics in rare event settings.
method Simulation study varying dataset sizes and event rates.
result AUC is unreliable in rare event settings but stable with moderate numbers of events.
LOBDIF predicts limit order book events using a diffusion model.
problem Predicting the timing and type of events in a dynamic market system.
method LOBDIF uses a diffusion model to learn the complex time-event distribution in limit order book streams.
result LOBDIF significantly outperforms existing methods in real-world data experiments.
Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement pred…
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.
A new boosting model handles dependent censoring in time-to-event data.
problem Independent censoring assumption leads to biased predictions in time-to-event analysis.
method Clayton-boost, a boosting approach using Clayton copula.
result Clayton-boost outperforms other methods in handling dependent censoring.
Generative model creates EEG data for RSVP experiments.
problem Limited EEG data for training deep learning models.
method Wasserstein Generative Adversarial Network (WGAN-GP) with gradient penalty.
result Improved event classification performance with class-conditioned WGAN-GP.