Hawkes processes are point processes that can be used to build probabilistic models to describe and predict occurrence patterns of random events. They are widely used in high-frequency trading, seismic analysis and neuroscience. A critical numerical calculation in Hawkes process models is parameter estimation, which is used to fit a Hawkes process model to a data set. The parameter estimation problem can be solved by searching for a parameter set that maximises the log-likelihood. A core operation of this search process, the log-likelihood evaluation, is computationally demanding if the number of data points is large. To accelerate the computation, we present a log-likelihood evaluation strategy which is suitable for hardware acceleration. We then design and optimise a pipelined engine based on our proposed strategy. In the experiments, an FPGA-based implementation of the proposed engine is shown to be up to 72 times faster than a single-core CPU, and 10 times faster than an 8-core CPU.
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