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Temporal Point Processes (TPPs) serve as the standard mathematical frameworkfor modeling asynchronous event sequences in continuous time. However,classical TPP models are often constrained by strong assumptions, limitingtheir ability to capture complex real-world event dynamics. To overcome thislimitation, researchers have proposed Neural TPPs, which leverage neuralnetwork parametrizations to offer more flexible and efficient modeling. Whilerecent studies demonstrate the effectiveness of Neural TPPs, they often lack aunified setup, relying on different baselines, datasets, and experimentalconfigurations. This makes it challenging to identify the key factors drivingimprovements in predictive accuracy, hindering research progress. To bridgethis gap, we present a comprehensive large-scale experimental study thatsystematically evaluates the predictive accuracy of state-of-the-art neural TPPmodels. Our study encompasses multiple real-world and synthetic event sequencedatasets, following a carefully designed unified setup. We thoroughlyinvestigate the influence of major architectural components such as eventencoding, history encoder, and decoder parametrization on both time and markprediction tasks. Additionally, we delve into the less explored area ofprobabilistic calibration for neural TPP models. By analyzing our results, wedraw insightful conclusions regarding the significance of history size and theimpact of architectural components on predictive accuracy. Furthermore, we shedlight on the miscalibration of mark distributions in neural TPP models. Ourstudy aims to provide valuable insights into the performance andcharacteristics of neural TPP models, contributing to a better understanding oftheir strengths and limitations.

On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data