{"total":83,"rows":[{"id":"2202.07125","title":"Transformers in Time Series: A Survey","created":"2022-02-15","cited_by_count":497,"area":null},{"id":"2310.10196","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","created":"2023-10-16","cited_by_count":110,"area":null},{"id":"2307.08097","title":"EasyTPP: Towards Open Benchmarking Temporal Point Processes","created":"2023-07-16","cited_by_count":68,"area":null},{"id":"2210.01753","title":"HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences","created":"2022-10-04","cited_by_count":57,"area":null},{"id":"2402.10350","title":"Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review","created":"2024-02-15","cited_by_count":53,"area":null},{"id":"2208.01874","title":"Exploring Generative Neural Temporal Point Process","created":"2022-08-03","cited_by_count":30,"area":null},{"id":"2310.04993","title":"Prompt-augmented Temporal Point Process for Streaming Event Sequence","created":"2023-10-08","cited_by_count":24,"area":null},{"id":"2301.12023","title":"Meta Temporal Point Processes","created":"2023-01-27","cited_by_count":17,"area":null},{"id":"2409.18158","title":"Decomposable Transformer Point Processes","created":"2024-09-26","cited_by_count":16,"area":null},{"id":"2406.14341","title":"HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?","created":"2024-06-20","cited_by_count":13,"area":null},{"id":"2312.07696","title":"Real-time Network Intrusion Detection via Decision Transformers","created":"2023-12-12","cited_by_count":12,"area":null},{"id":"2412.19634","title":"Deep Continuous-Time State-Space Models for Marked Event Sequences","created":"2024-12-27","cited_by_count":11,"area":null},{"id":"2310.17800","title":"Interacting Diffusion Processes for Event Sequence Forecasting","created":"2023-10-26","cited_by_count":10,"area":null},{"id":"2310.20218","title":"A Systematic Review for Transformer-based Long-term Series Forecasting","created":"2023-10-31","cited_by_count":10,"area":null},{"id":"2509.24762","title":"In-Context Learning of Temporal Point Processes with Foundation Inference Models","created":"2025-09-29","cited_by_count":10,"area":null},{"id":"2406.01124","title":"Latent Logic Tree Extraction for Event Sequence Explanation from LLMs","created":"2024-06-03","cited_by_count":9,"area":null},{"id":"2410.02062","title":"TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models","created":"2024-10-02","cited_by_count":9,"area":null},{"id":"2311.01033","title":"Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction","created":"2023-11-02","cited_by_count":7,"area":null},{"id":"2401.04612","title":"Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes","created":"2024-01-09","cited_by_count":7,"area":null},{"id":"2306.14072","title":"Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts","created":"2023-06-24","cited_by_count":6,"area":null},{"id":"2404.02047","title":"Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge","created":"2024-04-02","cited_by_count":6,"area":null},{"id":"2406.06149","title":"Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations","created":"2024-06-10","cited_by_count":6,"area":null},{"id":"2408.13131","title":"Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching","created":"2024-08-23","cited_by_count":6,"area":null},{"id":"2502.07139","title":"Byte-token Enhanced Language Models for Temporal Point Processes Analysis","created":"2025-02-11","cited_by_count":6,"area":null},{"id":"2502.01922","title":"LAST SToP For Modeling Asynchronous Time Series","created":"2025-02-04","cited_by_count":5,"area":null},{"id":"2404.03804","title":"TransformerLSR: Attentive Joint Model of Longitudinal Data, Survival, and Recurrent Events with Concurrent Latent Structure","created":"2024-04-04","cited_by_count":3,"area":null},{"id":"2501.14291","title":"Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches","created":"2025-01-24","cited_by_count":3,"area":null},{"id":"2508.01474","title":"HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens","created":"2025-08-02","cited_by_count":3,"area":null},{"id":"2303.00280","title":"Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention","created":"2023-03-01","cited_by_count":2,"area":null},{"id":"2307.16661","title":"Proactive Resource Request for Disaster Response: A Deep Learning-based Optimization Model","created":"2023-07-31","cited_by_count":2,"area":null},{"id":"2312.07338","title":"Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification","created":"2023-12-12","cited_by_count":2,"area":null},{"id":"2402.00987","title":"Self-Supervised Contrastive Pre-Training for Multivariate Point Processes","created":"2024-02-01","cited_by_count":2,"area":null},{"id":"2411.00621","title":"Nonparametric estimation of Hawkes processes with RKHSs","created":"2024-11-01","cited_by_count":2,"area":null},{"id":"2501.08653","title":"Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph","created":"2025-01-15","cited_by_count":2,"area":null},{"id":"2504.21795","title":"Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model","created":"2025-04-30","cited_by_count":2,"area":null},{"id":"2307.10305","title":"Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows","created":"2023-07-13","cited_by_count":1,"area":null},{"id":"2310.00574","title":"YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs","created":"2023-10-01","cited_by_count":1,"area":null},{"id":"2402.10392","title":"Pretext Training Algorithms for Event Sequence Data","created":"2024-02-16","cited_by_count":1,"area":null},{"id":"2410.10648","title":"A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2411.04397","title":"A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior","created":"2024-11-07","cited_by_count":1,"area":null},{"id":"2510.16161","title":"Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction","created":"2025-10-17","cited_by_count":1,"area":null},{"id":"2511.01096","title":"Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes","created":"2025-11-02","cited_by_count":1,"area":null},{"id":"2609.24579","title":"Universal Multi-Modal Traceformer: Integrating Heterogeneous Context for Process Event Prediction","created":"2026-09-21","cited_by_count":1,"area":null},{"id":"2302.06247","title":"Continuous-time convolutions model of event sequences","created":"2023-02-13","cited_by_count":0,"area":null},{"id":"2309.02868","title":"Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference","created":"2023-09-06","cited_by_count":0,"area":null},{"id":"2309.09443","title":"Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter","created":"2023-09-18","cited_by_count":0,"area":null},{"id":"2311.07797","title":"Explainable History Distillation by Marked Temporal Point Process","created":"2023-11-13","cited_by_count":0,"area":null},{"id":"2311.08141","title":"GMTR: Graph Matching Transformers","created":"2023-11-14","cited_by_count":0,"area":null},{"id":"2312.16805","title":"DarkShot: Lighting Dark Images with Low-Compute and High-Quality","created":"2023-12-28","cited_by_count":0,"area":null},{"id":"2401.05431","title":"TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing","created":"2024-01-06","cited_by_count":0,"area":null},{"id":"2401.05432","title":"TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks","created":"2024-01-06","cited_by_count":0,"area":null},{"id":"2403.13663","title":"T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image","created":"2024-03-20","cited_by_count":0,"area":null},{"id":"2408.17101","title":"Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithms","created":"2024-08-30","cited_by_count":0,"area":null},{"id":"2410.11162","title":"Towards General Deepfake Detection with Dynamic Curriculum","created":"2024-10-15","cited_by_count":0,"area":null},{"id":"2410.13409","title":"Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs","created":"2024-10-17","cited_by_count":0,"area":null},{"id":"2410.19512","title":"Marked Temporal Bayesian Flow Point Processes","created":"2024-10-25","cited_by_count":0,"area":null},{"id":"2412.08590","title":"Preventing Conflicting Gradients in Neural Marked Temporal Point Processes","created":"2024-12-11","cited_by_count":0,"area":null},{"id":"2502.04899","title":"Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects","created":"2025-02-07","cited_by_count":0,"area":null},{"id":"2502.13290","title":"Prediction of Clinical Complication Onset using Neural Point Processes","created":"2025-02-18","cited_by_count":0,"area":null},{"id":"2506.12842","title":"Uncovering Social Network Activity Using Joint User and Topic Interaction","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2507.06486","title":"Mask6D: Masked Pose Priors For 6D Object Pose Estimation","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2510.20414","title":"Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes","created":"2025-10-23","cited_by_count":0,"area":null},{"id":"2511.06032","title":"ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes","created":"2025-11-08","cited_by_count":0,"area":null},{"id":"2511.08939","title":"TransactionGPT","created":"2025-11-12","cited_by_count":0,"area":null},{"id":"2601.09220","title":"From Hawkes Processes to Attention: Time-Modulated Mechanisms for Event Sequences","created":"2026-01-14","cited_by_count":0,"area":null},{"id":"2602.01125","title":"Long-range Modeling and Processing of Multimodal Event Sequences","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2602.09079","title":"Patient foundation model for risk stratification in low-risk overweight patients","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2602.23629","title":"Multivariate Spatio-Temporal Neural Hawkes Processes","created":"2026-02-27","cited_by_count":0,"area":null},{"id":"2603.11462","title":"Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2604.15787","title":"EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2604.20374","title":"Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.00708","title":"Deep Kernel Learning for Stratifying Glaucoma Trajectories","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.14069","title":"SurF: A Generative Model for Multivariate Irregular Time Series Forecasting","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.17568","title":"Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2606.24982","title":"Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2606.30139","title":"Warranted Attention: Learning What to Pass from Attention to Prediction","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2606.30398","title":"ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2607.14733","title":"GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs","created":"2026-07-16","cited_by_count":0,"area":null},{"id":"2607.22781","title":"What Softmax Throws Away: Mass-Aware Attention for Evidence Accumulation","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2608.11555","title":"Certifying What Helps Customer-Return Timing: A Screen-and-Confirm Test for Conditioning Signals, and Why Decay Is Nearly Enough","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2610.12586","title":"Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.14948","title":"Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.17503","title":"Foundation Models for Event Prediction Across Heterogeneous Event Logs","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}