2 research outputs found
Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on the
assumption that sequential observations often share latent phenomena, while
also possessing idiosyncratic effects. An alternating optimization method is
proposed to learn a "registered" point process that accounts for shared
structure, as well as "warping" functions that characterize idiosyncratic
aspects of each observed sequence. Under reasonable constraints, in each
iteration we update the sample-specific warping functions by solving a set of
constrained nonlinear programming problems in parallel, and update the model by
maximum likelihood estimation. The justifiability, complexity and robustness of
the proposed method are investigated in detail, and the influence of sequence
stitching on the learning results is examined empirically. Experiments on both
synthetic and real-world data demonstrate that the method yields explainable
point process models, achieving encouraging results compared to
state-of-the-art methods
PoPPy: A Point Process Toolbox Based on PyTorch
PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible
designing and efficient learning of point process models. It can be used for
interpretable sequential data modeling and analysis, e.g., Granger causality
analysis of multi-variate point processes, point process-based simulation and
prediction of event sequences. In practice, the key points of point
process-based sequential data modeling include: 1) How to design intensity
functions to describe the mechanism behind observed data? 2) How to learn the
proposed intensity functions from observed data? The goal of PoPPy is providing
a user-friendly solution to the key points above and achieving large-scale
point process-based sequential data analysis, simulation and prediction