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In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t Designing a spam-filtering system that can run efficiently on heavily(More)
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(More)
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(More)
—Ordinal analysis is a statistical method for analysing the complexity of time series. This method has been used in characterising dynamic changes in time series, with various applications such as financial risk modelling and biomedical signal processing. Ordinal pattern encoding is a fundamental calculation in ordinal analysis. It is computationally(More)
Parallel genetic algorithms (pGAs) are a variant of genetic algorithms which can promise substantial gains in both efficiency of execution and quality of results. pGAs have attracted researchers to implement them in FPGAs, but the implementation always needs large human effort. To simplify the implementation process and make the hardware pGA designs(More)
Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially useful for solving complex non-linear and non-convex problems. However, the required execution time often limits their application to small-scale or latency-insensitive problems, so techniques to increase the computational efficiency of GAs are needed.(More)
Heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimation, or HAC estimation in short, is one of the most important techniques in time series analysis and forecasting. It serves as a powerful analytical tool for hypothesis testing and model verification. However , HAC estimation for long and high-dimensional time series is(More)