New STH distance finds patterns in event timeseries without resampling.
problem Lack of efficient analysis methods for event and state timeseries.
method Define STE-ts, propose STH, leveraging both time and state duration.
result Improved precision and computation time compared to resampled metrics.
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…
Time Series forecasting (univariate and multivariate) is a problem of high complexity due the different patterns that have to be detected in the input, ranging from high to low frequencies ones. In this paper we propose a new model for timeseries prediction that utilizes convolutional layers for feature extraction, a r…
Adaptive prediction timing improves healthcare outcomes by predicting patient events at the right frequency.
problem Inconsistent prediction granularity in healthcare models.
method Introduces a novel approach using Bayesian recurrent models and a new aggregation method to adapt prediction frequency based on uncertainty.
result Adaptive prediction timing leads to improved predictive performance, especially in the critical first 12 hours of patient stay.
Model predicts ESG ratings from news articles using multivariate timeseries analysis.
problem Lack of accurate and automated methods for ESG ratings prediction.
method Multivariate timeseries analysis combined with deep learning.
result Model outperforms state-of-the-art methods in predicting ESG ratings.
Parity calibration aims to predict increase-decrease events, not values.
problem Forecasting future increase-decrease events rather than exact values.
method Online binary calibration method to achieve parity calibration.
result Online binary calibration achieves parity calibration effectively.
Dynamic assessment of patient status (e.g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation. Extraction and cleaning of expert-selected clinical variables discards information and protrac…
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
problem Forecasting volatile retail sales trends with unknown factors.
method Benchmarked traditional ARIMA model against advanced models like Prophet and LightGBM on historical Walmart sales data.
result ARIMA model outperforms LightGBM and achieves computational efficiency.
We present an outlook of the studies on correlations in the price timeseries of stocks, discussing the construction and applications of "asset tree". The topic discussed here should illustrate how the complex economic system (financial market) enrichens the list of existing dynamical systems that physicists have been s…
TSGAN models time series data with limited data, outperforming competitors.
problem Lack of quality time series data for machine learning.
method Proposes Time Series GAN (TSGAN) for synthetic time series generation.
result TSGAN outperforms competitors in FID metric and qualitative classification.
We propose a simple quantitative model of Schumpeterian economic dynamics. New goods and services are endogenously produced through combinations of existing goods. As soon as new goods enter the market they may compete against already existing goods, in other words new products can have destructive effects on existing …
New methods improve brain data analysis from fMRI datasets.
problem Simplified brain models from correlational values are insufficient.
method Deep learning and geometric deep learning techniques.
result Improved predictive spatio-temporal brain data representation.
Paper separates financial time series into fast and slow components.
problem Multiscale behavior in financial time series data.
method Uses variance and tail stationarity criteria as generalized eigenvalue problems.
result Identifies slow and fast components in asset returns and prices.
New deep learning model estimates scattering timescale of FRBs efficiently.
problem Estimating scattering timescale of fast radio bursts (FRBs) is a bottleneck.
method Multimodal Transformer Based Generic Mixture Density Network (MT-GMDN) that ingests dynamic spectrum and timeseries profile.
result Achieves 94% R2 on expected value of τ for measurable scattering. CNN improves stock price prediction accuracy.
problem Predicting future stock price movements.
method Hybrid approach combining machine learning and CNN.
result CNN-based model outperforms other models.
AutoEncoder smooths noisy sensor data and interpolates missing values.
problem Noisy sensor data and missing timepoints require interpolation.
method Uses AutoEncoder to denoise and interpolate data.
result AutoEncoder improves data quality and reveals dynamics.
Periodicity is often studied in timeseries modelling with autoregressive methods but is less popular in the kernel literature, particularly for higher dimensional problems such as in textures, crystallography, and quantum mechanics. Large datasets often make modelling periodicity untenable for otherwise powerful non-pa…
Study analyzes stock market dynamics using Tsallis statistics and GHE, revealing pre-bubble and post-bubble market characteristics.
problem Understanding stock market dynamics and predicting market bubbles.
method Non-linear analysis using time-dependent Tsallis statistics and Generalized Hurst Exponents.
result Temporal trends of q-triplet values differ before and after market bubbles, indicating significant market dynamics changes.
XAI identifies key time steps for early crop classification.
problem Early crop classification with high accuracy and timeliness.
method Training a baseline model with LRP to identify important time steps.
result Identified a 21st April 2019 to 9th August 2019 timeframe with 0.75% accuracy loss.
WISDoM uses the Wishart distribution to analyze neurological data like EEG and brain connectivity.
problem Characterizing deviations of covariance or correlation matrices from expected values.
method WISDoM framework for quantifying deviations from the Wishart distribution.
result Validated on EEG feature ranking and classification of autism subjects.
Unified framework for self-supervised learning via latent distribution matching.
problem Lack of a unifying theoretical framework for diverse SSL methods.
method Casting SSL as latent distribution matching (LDM): maximizing alignment and uniformity.
result Derives a Bayesian filtering model and proves identifiable latent representations.
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.
Paper proposes a new trading strategy using corporate event detection from news articles.
problem Predicting stock movements based on corporate events from news articles.
method Bi-level event detection model: low-level for token-level event identification, high-level for article-level event identification.
result The proposed strategy outperforms existing models in stock prediction metrics.
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…
TPLVM models portfolio construction for non-Gaussian financial data.
problem Optimal asset allocation in finance with non-Gaussian fluctuations.
method Student's t-process latent variable model (TPLVM) for portfolio optimization.
result TPLVM outperforms Gaussian process latent variable model in minimum-variance portfolio construction.
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.
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.
Non-spanning identification of scheduled event risk in option pricing.
problem Separating continuous surface from scheduled jump in option pricing.
method Modeling FOMC decisions, CPI releases, and NFP reports as deterministic-time jumps in risk-neutral option pricing.
result Improves held-out event-spanning pricing with Gaussian and two-component mixture jumps.
New approach predicts event probabilities for better event detection.
problem Class imbalance and inaccurate event detection in time series analysis.
method Regression-based approach to predict probability densities at event locations.
result Regression-based approaches outperform segmentation-based methods.
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.
CAUSE learns Granger causality from event sequences, outperforming existing methods.
problem Learning Granger causality from complex, interdependent event sequences.
method CAUSE uses a neural point process to capture interdependency and an attribution method to extract Granger causality.
result CAUSE outperforms state-of-the-art methods in inferring inter-type Granger causality.
We present a windowed technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers interpretable spatiotemporal structure in data via non-smooth and non-convex optimization. In each time window, we assume the data follow a linear model parameterized …
REST framework predicts stock trends by considering stock-specific and related-stock events.
problem Predicting stock trends using event information from news, social media, and discussion boards.
method REST framework addresses two main shortcomings of existing event-driven methods: stock-specific event influence and related-stock event influence.
result REST framework achieves higher investment returns compared to baselines.
GANs improve event generation in physics experiments.
problem Improving statistical precision in event generation.
method Used generative adversarial networks (GANs) to generate events.
result GANs amplify the statistical precision of the training sample.
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…
New deep learning method handles rare and imbalanced events in time series.
problem Challenges in event detection in time series data, especially rare and imbalanced events.
method Supervised regression-based deep learning approach that handles various types of events.
result Superior performance across diverse domains, particularly for rare events and imbalanced datasets.
Combining multiple collider events improves machine learning performance.
problem Improving machine learning for collider physics.
method Study of single-event vs multi-event classifiers under IID assumption.
result Training single-event classifiers is more effective than multi-event classifiers.
The study uses financial events to predict stock market movements.
problem Predicting stock market movements using financial events.
method Combined event extraction method, BERT/ALBERT enhanced event representation, and extended hierarchical attention network.
result Significantly better accuracies and higher simulated returns compared to state-of-the-art models.
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.
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.
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as sleep stages, and micro-events (<2s) such as spindles and K-complexes. Annotations of such events require a trained sleep expert, a time co…
Neural network model predicts alternating event-free periods.
problem Dynamic prediction of alternating recurrent events with statistical nuance.
method Developed an online dynamic prediction framework using neural network theory.
result Outstanding performance in predicting alternating recurrent event-free time.
The plausibility of uncommon events and miracles based on testimony of such an event has been much discussed. When analyzing the probabilities involved, it has mostly been assumed that the common events can be taken as data in the calculations. However, we usually have only testimonies for the common events. While this…
Paper tackles event stream outliers using a novel weight function.
problem Learning event streams with unexpected absences or occurrences.
method Temporal point process framework with a novel weight function.
result The proposed method effectively handles both commission and omission outliers.
Traditional event detection classifies a word or a phrase in a given sentence for a set of predefined event types. The limitation of such predefined set is that it prevents the adaptation of the event detection models to new event types. We study a novel formulation of event detection that describes types via several k…
A fast geometric regularizer improves event camera performance.
problem Event collapse in contrast maximization framework.
method Geometric regularizer to mitigate overfitting.
result State-of-the-art accuracy with reduced computational complexity.
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.
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…