Reconfigurable predictive systems for event streams

• Main Author: Guo, Ce
• Other Authors: Luk, Wayne
• Published: Imperial College London 2016
• Subjects: 005.3
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.724173

The study of event streams involves modelling arrival times of discrete random events. Predictive systems for event streams infer information about future occurrences of random events. These systems are useful for various applications such as stock trading modelling and earthquake analysis, but the computational burdens limit their predictive power. This thesis addresses the design and optimisation of reconfigurable predictive systems for event streams. The first contribution of this thesis is the reconfigurable acceleration solution for the calculations in the Hawkes point process models. We propose reconfigurable dataflow engines for intensity evaluation and likelihood evaluation for univariate and multivariate Hawkes point process models. We also establish an efficient collaboration scheme between the CPU platform and the reconfigurable accelerator to speed up parameter estimation. The second contribution of this thesis is a novel predictive model for event streams. As the Hawkes point process models have limited predictive accuracy and low computational efficiency, we propose a predictive model for event streams using regression techniques. We derive the model from the intensity function of the Hawkes point process, but the final form of the proposed model works without point process models. A software system based on the proposed model reduces the prediction error by 3\%--7\% compared to a system based on the Hawkes process. A hardware-accelerated system based on the proposed model is 5--66 times faster in model fitting compared to the accelerated system based on the Hawkes process, while the two systems produce similar predictive accuracy. The third contribution of this thesis is the design of two reconfigurable accelerators for time series analysis. One accelerator is for the estimation of correlation for multivariate time series. The other accelerator is for the ordinal pattern encoding for univariate time series. We design the two accelerators by transforming the calculations of the corresponding statistical metrics into pipeline-friendly forms. Compared to multicore CPUs, both accelerators show high efficiency for large time series data.