Temporal Point Processes

Follow this topic Ask about this topic

Momentum

0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 18

All topics

Certifying What Helps Customer-Return Timing: A Screen-and-Confirm Test for Conditioning Signals, and Why Decay Is Nearly Enough
Aug 12, 2026Sang Su Lee, Vineeth Loganathan, Shishir Dash +1Event Time Prediction Temporal Point Processes
Thumbtack, Inc. San Francisco, CA, USA
Smooth Neural Point Processes via B-Splines
Jul 23, 2026Michele Bellomo, Riccardo Ramaschi, Alberto Dolara +1Temporal Point Processes
Politecnico di Milano Milan, Italy · University College London London, UK
ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
Jul 14, 2026Saiyue Lyu, Zhitian Zhang, Ruizhi Deng +1Irregular Time-Series Modeling Event Time Prediction
Department of Computer Science, University of British Columbia · Work done during an internship at RBC Borealis. · RBC Borealis
NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
Jul 13, 2026Neha Gupta, Aditya MaheshwariTemporal Point Processes Parameter Estimation
Operations Management and Quantitative Techniques Area, Indian Institute of Management Indore, Indore 453556, India.
From Jumps to Signatures: a Generative Method for Temporal Point Processes
Jul 7, 2026Niels Cariou-Kotlarek, Vasileios LamposTemporal Point Processes Generative Modeling
Centre for Artificial Intelligence Department of Computer Science University College London
Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns
Jul 7, 2026Sishun Liu, Sajal Halder, Ke Deng +2Social Media Analysis Temporal Point Processes
RMIT University, Melbourne, Victoria 3000, Australia · Macquarie University, Sydney, New South Wales 2000, Australia
Efficient Temporal Point Processes via Monotone Alternating Splines
Jul 2, 2026Cheng Wan, Quyu Kong, Feng ZhouTemporal Point Processes
Hong Kong University of Science and Technology, Hong Kong · Alibaba Cloud, Hangzhou, China · Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
Jul 1, 2026Yahya Aalaila, Mouad Elhamdi, Gerrit Großmann +3Temporal Point Processes Spatial Generalization
1German Research Center for Artificial Intelligence (DFKI) · 5Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU) · 2Universit´e Mohammed VI Polytechnique +2
Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Jul 1, 2026Yahya Aalaila, Gerrit Großmann, Sebastian VollmerTemporal Point Processes
German Research Center for Artificial Intelligence (DFKI), Data Science and its Applications Research Group, Kaiserslautern, Germany · Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU), Kaiserslautern, Germany
Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation
Jun 23, 2026Shuai Zhang, Yancheng Chen, Chuan Zhou +5Temporal Point Processes Latent Diffusion Models
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China · Department of Data Science, City University of Hong Kong, Hong Kong 999077, China +1
HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity
Jun 15, 2026Yahya Aalaila, Sumantrak Mukherjee, Gerrit Großmann +1Temporal Point Processes Synthetic Benchmark Generation
German Research Center for Artificial Intelligence (DFKI), Data Science and its Applications Research Group, Kaiserslautern, Germany
Non-Negative Matrix Factorization for Event Data
Jun 4, 2026Raphaël RomeroTemporal Point Processes Latent Variable Models
Ghent University · Ghent, Belgium
GLIDE: Graph-guided Leap Inference for Diffusion Estimation of Spatio-Temporal Point Processes
May 31, 2026Guanyu Zhou, Yao Liu, Yanglei Gan +5Event Time Prediction Temporal Point Processes
University of Electronic Science and Technology of China, Chengdu, China
Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
May 17, 2026Zhitong Xu, Qiwei Yuan, Yinghao Chen +2Temporal Point Processes Neural Network Interpretability
Kahlert School of Computing, University of Utah · Celonis AI
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
May 13, 2026Mohammad R. Rezaei, Tejas Balaji, Rahul G. KrishnanIrregular Time-Series Modeling Multivariate Time Series Forecasting
Department of Computer Science University of Toronto Vector Institute Toronto, ON, Canada
On Observation Time for Recovering Latent Hawkes Networks
May 8, 2026Jonas Linkerhägner, Michele Bortolasi, Lorenzo Baldassari +2Temporal Point Processes Sample Complexity
University of Basel · Rice University
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
May 2, 2026Keyan Chen, Qiwei Yuan, Zhitong Xu +2Temporal Point Processes Flow Matching
Kahlert School of Computing University of Utah · Celonis AI
Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols
Apr 22, 2026Huaiyu Jia, Jiehshun You, Yizhi Luo +2Event Time Prediction Temporal Point Processes
HKUST(GZ) · GUANGZHOU, China · Hong Kong Polytechnic University +1

Aug 12, 2026, cs.LGJ/K move · Enter open · S save

Certifying What Helps Customer-Return Timing: A Screen-and-Confirm Test for Conditioning Signals, and Why Decay Is Nearly Enough

Sang Su Lee, Vineeth Loganathan, Shishir Dash, Vijay Raghavan

Thumbtack, Inc. San Francisco, CA, USA

Event Time Prediction Temporal Point Processes Statistical Hypothesis Testing

Open paper AskPDF arXiv

Practitioners enrich customer-return models with ever more signals (lifetime value, category, recency/frequency, calendar, geography), and the temporal-point-process (TPP) literature follows suit with covariate- and external-covariate-conditioned intensities. But does any of it improve the timing, and how would you know? A null ("feature X doesn't help") is only meaningful if the model could have found a signal. We make two contributions--a method and a measurement--to answer this credibly. (i) A screen-and-confirm protocol that certifies whether a candidate signal improves a TPP's event-timing likelihood: a positive control plants a coupling of known strength and confirms the model recovers it, so a real-data null can be read as "no signal" rather than "weak method." The control is validated for categorical and continuous encodings, and on a real clock-driven dataset (NYC taxi hour-of-day). (ii) A model-free ceiling quantifying how little of customer-return timing is point-predictable at all (a single-digit percentage of gap variance from any covariate; returns are near-memoryless). With these we certify a clean result on three public benchmarks (Amazon, Taobao, RetailRocket) and a real marketplace (Thumbtack): the inter-event clock--continuous-time decay, long known to beat frozen-intensity models--is nearly sufficient, and the conditioning the field keeps adding is redundant or harmful on top of it (statistically null on the public benchmarks, at most 0.06 NLL; null to mildly harmful on the marketplace). We do not claim to discover that decay helps; our contribution is the tools that turn "conditioning doesn't help" into a checkable, certified statement--plus an honest-evaluation account of the read-out/leakage pitfalls we hit and retracted.

Momentum

Related topics

Latest papers 18