Introduces Indep and Correl models for correlated arrivals in online matching and develops algorithms with constant-factor guarantees that outperform fluid relaxations on high-variance data.
Hawkes Processes
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abstract
Hawkes processes are a particularly interesting class of stochastic process that have been applied in diverse areas, from earthquake modelling to financial analysis. They are point processes whose defining characteristic is that they 'self-excite', meaning that each arrival increases the rate of future arrivals for some period of time. Hawkes processes are well established, particularly within the financial literature, yet many of the treatments are inaccessible to one not acquainted with the topic. This survey provides background, introduces the field and historical developments, and touches upon all major aspects of Hawkes processes.
verdicts
UNVERDICTED 3representative citing papers
SynHAT uses a novel two-stage spatio-temporal diffusion framework with Latent Spatio-Temporal U-Net to synthesize realistic human activity traces, outperforming baselines by 52% on spatial and 33% on temporal metrics across four cities.
Time2Vec learns a vector representation of time that improves model performance when used in place of raw time inputs across various models and problems.
citing papers explorer
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A Nonparametric Framework for Online Stochastic Matching with Correlated Arrivals
Introduces Indep and Correl models for correlated arrivals in online matching and develops algorithms with constant-factor guarantees that outperform fluid relaxations on high-variance data.
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SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces
SynHAT uses a novel two-stage spatio-temporal diffusion framework with Latent Spatio-Temporal U-Net to synthesize realistic human activity traces, outperforming baselines by 52% on spatial and 33% on temporal metrics across four cities.
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Time2Vec: Learning a Vector Representation of Time
Time2Vec learns a vector representation of time that improves model performance when used in place of raw time inputs across various models and problems.