一种时序数据多项式拟合加速方法

考虑到时序数据自身特点，特别是针对周期采样的时序数据，本文提出一种多项式拟合加速方法，讨论了平均分段和非平均分段两种情况下时序数据多项式拟合方法，通过重复利用部分矩阵的中间计算结果，大幅提高了多项式拟合的计算速度.实验结果表明，对于周期采样数据，该方法在平均分段和非平均分段时最多可分别获得约28倍和17倍计算加速. 

Polynomial Fitting Acceleration Method Based on Time-Series Segmentation

Polynomial fitting is one of the most important approaches of time-series data compres-sion,and the calculation process involves multiple matrix multiplication and inversion,which lead to high computational complexity.Due to the large number of time series instances,the incoming data need to be high performance and real-time processing.Considering the characteristics of time-series data,especially for the periodic sampled time series data,a polynomial fitting acceler-ation method was proposed.Reusing the intermediate calculation results,both of polynomial fit-ting methods with fixed and variable segment length were taken to speed up the polynomial fitting of time-series data.Experimental results show that the proposed method can achieve a speedup of 28x and 17x for fixed length segmentation and variable segmentation respectively.