Implementation of "Detecting Anomalous Event Sequences with Temporal Point Processes" (NeurIPS 2021)
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Updated
Dec 30, 2021 - Jupyter Notebook
Implementation of "Detecting Anomalous Event Sequences with Temporal Point Processes" (NeurIPS 2021)
This repository contains recent background materials, current works, and codes for researching in TPP.
Code and real data for "Counterfactual Temporal Point Processes", NeurIPS 2022
Implementation of "Neural Jump-Diffusion Temporal Point Processes" (ICML 2024 Spotlight)
PyTorch-Lightning implementation of Meta Temporal Point Processes
The official Pytorch implementation of paper "Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks""
Implementation of "Conformal Anomaly Detection in Event Sequences" (ICML 2025)
Master thesis title: "Towards Event Sequence Foundation Models: exploring temporal point process transformers for power grid fault prediction" Dataset and modelling infrastructure for modelling "event streams": sequences of continuous time, multivariate events with complex internal dependencies.
Dual Network Hawkes Process -- Analyzing Topic Transitions in Text-Based Social Cascades
Implementation of "Multiple Hypothesis Testing for Anomaly Detection in Multi-type Event Sequences" (ICDM 2023)
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