Marked Temporal Bayesian Flow Point Processes

• URL: http://arxiv.org/abs/2410.19512v1
• Date: Fri, 25 Oct 2024 12:32:43 GMT
• Title: Marked Temporal Bayesian Flow Point Processes
• Authors: Hui Chen, Xuhui Fan, Hengyu Liu, Longbing Cao,
• Abstract summary: Marked event data captures events by recording their continuous-valued occurrence timestamps along with their corresponding discrete-valued types. In this paper, we propose a novel generative MTPP model called BMTPP.
• Score: 32.04732953059373
• License:
• Abstract: Marked event data captures events by recording their continuous-valued occurrence timestamps along with their corresponding discrete-valued types. They have appeared in various real-world scenarios such as social media, financial transactions, and healthcare records, and have been effectively modeled through Marked Temporal Point Process (MTPP) models. Recently, developing generative models for these MTPP models have seen rapid development due to their powerful generative capability and less restrictive functional forms. However, existing generative MTPP models are usually challenged in jointly modeling events' timestamps and types since: (1) mainstream methods design the generative mechanisms for timestamps only and do not include event types; (2) the complex interdependence between the timestamps and event types are overlooked. In this paper, we propose a novel generative MTPP model called BMTPP. Unlike existing generative MTPP models, BMTPP flexibly models marked temporal joint distributions using a parameter-based approach. Additionally, by adding joint noise to the marked temporal data space, BMTPP effectively captures and explicitly reveals the interdependence between timestamps and event types. Extensive experiments validate the superiority of our approach over other state-of-the-art models and its ability to effectively capture marked-temporal interdependence.

Related papers

• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models [0.0]
  Temporal point processes (TPPs) are widely used to model the timing and occurrence of events in domains such as social networks, transportation systems, and e-commerce. We introduce TPP-LLM, a novel framework that integrates large language models (LLMs) with TPPs to capture both the semantic and temporal aspects of event sequences.
  arXiv  Detail & Related papers  (2024-10-02T22:17:24Z)
• UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting [98.12558945781693]
  We propose a transformer-based model UniTST containing a unified attention mechanism on the flattened patch tokens. Although our proposed model employs a simple architecture, it offers compelling performance as shown in our experiments on several datasets for time series forecasting.
  arXiv  Detail & Related papers  (2024-06-07T14:39:28Z)
• Cumulative Distribution Function based General Temporal Point Processes [49.758080415846884]
  CuFun model represents a novel approach to TPPs that revolves around the Cumulative Distribution Function (CDF) Our approach addresses several critical issues inherent in traditional TPP modeling. Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction.
  arXiv  Detail & Related papers  (2024-02-01T07:21:30Z)
• Interacting Diffusion Processes for Event Sequence Forecasting [20.380620709345898]
  We introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPP.
  arXiv  Detail & Related papers  (2023-10-26T22:17:25Z)
• ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction [69.74158631862652]
  Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. Existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. We propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations.
  arXiv  Detail & Related papers  (2023-09-22T14:26:06Z)
• Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference [21.51753838306655]
  Temporal point processes(TPPs) are the standard method for modeling such. Existing TPP models have focused on the conditional distribution of future events instead of explicitly modeling event interactions, imposing challenges for event predictions. We propose a novel approach that leverages a Neural Inference (NRI) to learn a graph that infers interactions while simultaneously learning dynamics patterns from observational data.
  arXiv  Detail & Related papers  (2023-09-06T09:47:03Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes [1.8749305679160366]
  Conditioned on past events, marked TPPs aim to learn the joint distribution of the time and the mark of the next event. For simplicity, conditionally independent TPP models assume time and marks are independent given event history. We model the conditional inter-dependence of time and mark to overcome the limitations of conditionally independent models.
  arXiv  Detail & Related papers  (2022-10-27T10:01:27Z)
• Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes [25.074394338483575]
  A large fraction of data generated via human activities can be represented as a sequence of events over a continuous-time. Deep learning models over these continuous-time event sequences is a non-trivial task. In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
  arXiv  Detail & Related papers  (2022-06-23T18:23:20Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.