Pipelined HAC Estimation Engines for Multivariate Time Series

被引:0
作者
Ce Guo
Wayne Luk
机构
[1] Imperial College London,Department of Computing
来源
Journal of Signal Processing Systems | 2014年 / 77卷
关键词
Time series; HAC estimation; Big data; Acceleration engine; FPGA;
D O I
暂无
中图分类号
学科分类号
摘要
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 computationally expensive. This paper describes a pipeline-friendly HAC estimation algorithm derived from a mathematical specification, by applying transformations to eliminate conditionals, to parallelise arithmetic, and to promote data reuse in computation. We discuss an initial hardware architecture for the proposed algorithm, and propose two optimised architectures to improve the worst-case performance. Experimental systems based on proposed architectures demonstrate high performance especially for long time series. One experimental system achieves up to 12 times speedup over an optimised software system on 12 CPU cores.
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页码:117 / 129
页数:12
相关论文
共 26 条
[1]  
Jegadeesh N(1993)Returns to buying winners and selling losers: implications for stock market efficiency The Journal of Finance 48 65-91
[2]  
Titman S(2009)Expected stock returns and variance risk premia Review of Financial Studies 22 4463-4492
[3]  
Bollerslev T(2011)What segments equity markets. Review of Financial Studies 24 3841-3890
[4]  
Tauchen G(1987)A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix Econometrica: Journal of the Econometric Society 55 703-708
[5]  
Zhou H(1994)Automatic lag selection in covariance matrix estimation Review of Economic Studies 61 631-653
[6]  
Bekaert G(1991)Heteroskedasticity and autocorrelation consistent covariance matrix estimation Econometrica: Journal of the Econometric Society 59 817-858
[7]  
Harvey C(2009)Accelerated fluctuation analysis by graphic cards and complex pattern formation in financial markets New Journal of Physics 11 093024-280
[8]  
Lundblad C(2011)Correlation analysis on GPU systems using NVIDIA’s CUDA Journal of Real-Time Image Processing 6 275-106
[9]  
Siegel S(1986)Induction of decision trees Machine Learning 1 81-631
[10]  
Newey WK(1984)Systolic matrix and vector multiplication methods for signal processing IEE Proceedings 131 623-undefined