Accelerating parameter estimation for multivariate self-exciting point processes

Abstract

Self-exciting point processes are stochastic processes capturing occurrence patterns of random events. They offer powerful tools to describe and predict temporal distributions of random events like stock trading and neurone spiking. A critical calculation in self-exciting point process models is parameter estimation, which fits a model to a data set. This calculation is computationally demanding when the number of data points is large and when the data dimension is high. This paper proposes the first reconfigurable computing solution to accelerate this calculation. We derive an acceleration strategy in a mathematical specification by eliminating complex data dependency, by cutting hardware resource requirement, and by parallelising arithmetic operations. In our experimental evaluation, an FPGA-based implementation of the proposed solution is up to 79 times faster than one CPU core, and 13 times faster than the same CPU with eight cores.