arXiv Machine Learning
Jul 3
Efficient Temporal Point Processes via Monotone Alternating Splines
arXiv:2607. 01752v1 Announce Type: new Abstract: Temporal point processes (TPPs) have widespread applications across various domains.
Smooth Neural Point Processes via B-Splines
arXiv:2607. 21098v1 Announce Type: new Abstract: Temporal point processes (TPPs) provide a general and flexible framework for modeling sequences of events in continuous time.
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arXiv Machine Learning
Jun 5
Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
arXiv:2501. 14291v3 Announce Type: replace Abstract: Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time.
arXiv Machine Learning
Sep 21
Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes
arXiv:2609.21382v1 Announce Type: new Abstract: Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability...
arXiv Machine Learning
Jul 31
Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework
arXiv:2607. 28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing.
arXiv Machine Learning
Jul 7
Advances in Neural Controlled Differential Equations
arXiv:2607. 05280v1 Announce Type: new Abstract: Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences.
arXiv Machine Learning
Jun 9
In-Context Learning of Temporal Point Processes with Foundation Inference Models
arXiv:2509. 24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events.
arXiv Machine Learning
Jul 9
From Jumps to Signatures: a Generative Method for Temporal Point Processes
arXiv:2607. 06652v1 Announce Type: new Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions.
arXiv Machine Learning
Jun 5
MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
arXiv:2511. 20577v5 Announce Type: replace Abstract: Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behavior expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends.
arXiv Machine Learning
Sep 22
Time Series Foundation Models for Process Model Forecasting
arXiv:2512.07624v2 Announce Type: replace Abstract: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of...
arXiv AI
Sep 16
Continuous-Time Machine Learning: A Unified Mathematical Perspective
The paper surveys continuous‑time (CT) machine learning, a framework for modeling temporal dynamics as continuous processes, especially useful when data are sampled irregularly or over long horizons. It introduces a unified taxonomy that groups major CT methods by their underlying mathematical formulations and shows how different architectural choices—such as vector‑field parameterization, stochasticity, memory mechanisms, and discretization—relate these families. The survey compares training algorithms, optimization strategies, failure modes, computational complexity, and benchmarks, reviews supporting software ecosystems, and outlines open challenges and future research directions.
arXiv Machine Learning
Jul 14
NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
arXiv:2607. 11177v1 Announce Type: new Abstract: In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captures long-range dependence through a fractional Mittag-Leffler excitation kernel.
Hugging Face Trending Papers
Sep 15
Continuous-Time Machine Learning: A Unified Mathematical Perspective
Continuous-time (CT) machine learning has emerged as a principled framework for modeling temporal dynamics as a continuous process, particularly when observations are sampled at arbitrary time points...