Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
Prompt-augmented temporal point process for streaming event sequence
Abstract Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling
continuous-time event sequences, such as user activities on the web and financial …
continuous-time event sequences, such as user activities on the web and financial …
Leveraging large language models for pre-trained recommender systems
Recent advancements in recommendation systems have shifted towards more
comprehensive and personalized recommendations by utilizing large language models …
comprehensive and personalized recommendations by utilizing large language models …
Db-gpt: Empowering database interactions with private large language models
The recent breakthroughs in large language models (LLMs) are positioned to transition
many areas of software. Database technologies particularly have an important entanglement …
many areas of software. Database technologies particularly have an important entanglement …
Easytpp: Towards open benchmarking the temporal point processes
Continuous-time event sequences play a vital role in real-world domains such as
healthcare, finance, online shopping, social networks, and so on. To model such data …
healthcare, finance, online shopping, social networks, and so on. To model such data …
Bellman meets hawkes: Model-based reinforcement learning via temporal point processes
We consider a sequential decision making problem where the agent faces the environment
characterized by the stochastic discrete events and seeks an optimal intervention policy …
characterized by the stochastic discrete events and seeks an optimal intervention policy …
Enhancing event sequence modeling with contrastive relational inference
Neural temporal point processes (TPPs) have shown promise for modeling continuous-time
event sequences. However, capturing the interactions between events is challenging yet …
event sequences. However, capturing the interactions between events is challenging yet …
Deep optimal timing strategies for time series
C Pan, F Zhou, X Hu, X Zhu, W Ning, Z Zhuang… - arXiv preprint arXiv …, 2023 - arxiv.org
Deciding the best future execution time is a critical task in many business activities while
evolving time series forecasting, and optimal timing strategy provides such a solution, which …
evolving time series forecasting, and optimal timing strategy provides such a solution, which …
Learning large-scale universal user representation with sparse mixture of experts
Learning user sequence behaviour embedding is very sophisticated and challenging due to
the complicated feature interactions over time and high dimensions of user features. Recent …
the complicated feature interactions over time and high dimensions of user features. Recent …
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Asynchronous time series, also known as temporal event sequences, are the basis of many
applications throughout different industries. Temporal point processes (TPPs) are the …
applications throughout different industries. Temporal point processes (TPPs) are the …