Papers › Prompt-augmented Temporal Point Process for Streaming Event Sequence

Prompt-augmented Temporal Point Process for Streaming Event Sequence

8 Oct 2023NeurIPS 2023 11arXiv:2310.04993archive 2025-07-28

Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In real-world applications, event data is typically received in a \emph{streaming} manner, where the distribution of patterns may shift over time. Additionally, \emph{privacy and memory constraints} are commonly observed in practical scenarios, further compounding the challenges. Therefore, the continuous monitoring of a TPP to learn the streaming event sequence is an important yet under-explored problem. Our work paper addresses this challenge by adopting Continual Learning (CL), which makes the model capable of continuously learning a sequence of tasks without catastrophic forgetting under realistic constraints. Correspondingly, we propose a simple yet effective framework, PromptTPP\footnote{Our code is available at {\small \url{ https://github.com/yanyanSann/PromptTPP}}}, by integrating the base TPP with a continuous-time retrieval prompt pool. The prompts, small learnable parameters, are stored in a memory space and jointly optimized with the base TPP, ensuring that the model learns event streams sequentially without buffering past examples or task-specific attributes. We present a novel and realistic experimental setup for modeling event streams, where PromptTPP consistently achieves state-of-the-art performance across three real user behavior datasets.

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ConTEncoderLayer yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) ran Apache-2.0 (permissive) · 4e3761095e10d6be · report
create_torch_dataloader yanyanSann/PromptTPP/model_run/neural_tpp/preprocess/torch_dataset.py community (archive-listed) ran Apache-2.0 (permissive) · 9d00011dad2cc74b · report
load_experiment_ids yanyanSann/PromptTPP/model_run/neural_tpp/autotuner.py community (archive-listed) ran fingerprinted Apache-2.0 (permissive) · 10d4c062b41c0f97 · report
prefix_attention yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · adbbd76b3e0b6ad1 · report
set_device yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) ran · our draft was wrong Apache-2.0 (permissive) · 2e7ad66f463619d1 · report
ContTPromptPool yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) unverified Apache-2.0 (permissive) · feb2e19c667683f4 · report
ContTPrxfixPromptLayer yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) unverified Apache-2.0 (permissive) · 052117f1ee931d5c · report
EventSampler yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) unverified Apache-2.0 (permissive) · 2f83d36760ee9645 · report
PromptAttNHP yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) unverified Apache-2.0 (permissive) · b56a6348507bdaf9 · report
TorchBaseModel yanyansann/prompttpp/model_run/neural_tpp/model/torch_model/torch_pro_anhp.py community (archive-listed) unverified Apache-2.0 (permissive) · c02945e70ba3b094 · report
TransformerCell yangalan123/anhp-andtt/andtt/neural/cell.py found in paper text by Syntology ran · metamorphic tier: deterministic MIT (permissive) · 2781387a8f9bc90d · report

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