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Tag: temporal

  • Multivariate Spatio-Temporal Neural Hawkes Processes

    Multivariate Spatio-Temporal Neural Hawkes Processes arXiv:2602.23629v1 Announce Type: new Abstract: We propose a Multivariate Spatio-Temporal Neural Hawkes Process for modeling complex multivariate event data with spatio-temporal dynamics. The proposed model extends continuous-time neural Hawkes processes by integrating spatial information into latent state evolution through learned temporal and spatial decay dynamics, enabling flexible modeling of excitation…

  • Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders

    Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders arXiv:2512.06348v1 Announce Type: new Abstract: Extreme weather events are widely studied in fields such as agriculture, ecology, and meteorology. The spatio-temporal co-occurrence of extreme events can strengthen or weaken under changing climate conditions. In this paper, we propose a novel approach to model spatio-temporal extremes by integrating climate…

  • Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide

    Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide Comparing model-free and model-based RL methods on a dynamic grid world The post Temporal-Difference Learning and the Importance of Exploration: An Illustrated Guide appeared first on Towards Data Science. Ryan Pégoud Go to original source

  • Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics

    Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics arXiv:2506.20935v1 Announce Type: new Abstract: Forecasting geopolitical conflict from data sources like the Global Database of Events, Language, and Tone (GDELT) is a critical challenge for national security. The inherent sparsity, burstiness,…

  • Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation

    Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation arXiv:2506.12183v1 Announce Type: new Abstract: Evaluating anomaly detection in multivariate time series (MTS) requires careful consideration of temporal dependencies, particularly when detecting subsequence anomalies common in fault detection scenarios. While time series cross-validation (TSCV) techniques aim to preserve temporal ordering during model evaluation, their impact…

  • Using Deep Operators to Create Spatio-temporal Surrogates for Dynamical Systems under Uncertainty

    Using Deep Operators to Create Spatio-temporal Surrogates for Dynamical Systems under Uncertainty arXiv:2506.11761v1 Announce Type: new Abstract: Spatio-temporal data, which consists of responses or measurements gathered at different times and positions, is ubiquitous across diverse applications of civil infrastructure. While SciML methods have made significant progress in tackling the issue of response prediction for individual…

  • STACI: Spatio-Temporal Aleatoric Conformal Inference

    STACI: Spatio-Temporal Aleatoric Conformal Inference arXiv:2505.21658v1 Announce Type: new Abstract: Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while scalable, typically assume a simplistic independent covariance matrix for the response, failing to capture the underlying correlation structure. However, spatio-temporal GPs suffer from issues of scalability…

  • Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA

    Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA arXiv:2501.12582v1 Announce Type: new Abstract: Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high-dimensional time-series data are challenging tasks in study of real-world complex systems, which demand interpretable data representations to facilitate comprehension of both spatial and temporal information…

  • Introducing n-Step Temporal-Difference Methods

    Introducing n-Step Temporal-Difference Methods Dissecting “Reinforcement Learning” by Richard S. Sutton with custom Python implementations, Episode V Continue reading on Towards Data Science » Oliver S Go to original source