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.
Develops a neural model to predict event occurrence and timing.
Neural network model predicts alternating event-free periods.
Proposes a model for predicting events from event streams.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
Models predict fire and other emergencies in Edmonton.
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…
In predictive process analytics, current and historical process data in event logs is used to predict the future, e.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction m…
Paper proposes a new trading strategy using corporate event detection from news articles.
LOBDIF predicts limit order book events using a diffusion model.
New methods for inferring, predicting, and estimating continuous-time, discrete-event processes.
The method learns to partition event time space for better prediction.
This monograph introduces deep learning models for predicting time-to-event outcomes.
We show social events can be accurately predicted, but often undesirably.
Neural model uses deductive database to predict events from past patterns.
SurvSurf predicts first hitting times for intermittent events without monotonic violations.
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on pred…
In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance. As a result, ad event prediction systems are vital and widely used for sponsored search and display advertising as well as Real-Time Bidding (RTB). In this work, we introduce an enhanced …
Modeling solar ramping events with spatio-temporal point processes.
Model uses LLM features to predict stock returns effectively.
We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynamics, i.e., when the next event wil…
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee t…
Simple tabular event prediction model outperforms existing methods.
Study compares resampling methods for rare event prediction in longitudinal studies.
New approach predicts event probabilities for better event detection.
Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times, or relative risk. Model calibration is relatively under explored, despite it…
Click through rate (CTR) prediction is very important for Native advertisement but also hard as there is no direct query intent. In this paper we propose a large-scale event embedding scheme to encode the each user browsing event by training a Siamese network with weak supervision on the users' consecutive events. The …
Model predicts epileptic seizures with high accuracy using EEG signals.
Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a…
Meta-learning improves event prediction from short sequences.
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…
SMURF-THP improves Transformer Hawkes process models by providing uncertainty quantification.
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
This paper evaluates data enrichment techniques for rare event detection in manufacturing.
Develops methods for spectral estimation and rare-event prediction in complex systems.
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…
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according…
In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event fro…
Esports have become major international sports with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unkno…
Proposes a new model for time-to-event prediction with uncertainty quantification.
Adaptive prediction timing improves healthcare outcomes by predicting patient events at the right frequency.
Study shows pre-event L2 liquidity state predicts crypto futures liquidity better than event labels.
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…
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…
The study uses financial events to predict stock market movements.
Model compresses event-like contexts using gated surprise signals.
This document describes an approach to the problem of predicting dangerous seismic events in active coal mines up to 8 hours in advance. It was developed as a part of the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. The solutions presented consist of ensembles of various pred…
This paper uses MIL and MHCNN-RNN to predict precursors to aviation safety events.
Study examines HTE estimation from time-to-event data with competing events.