论文标题
基于周期性指数的多项式近似的记忆预测因子有限
Limited memory predictors based on polynomial approximation of periodic exponents
论文作者
论文摘要
本文为有限的内存时间传输函数提供了连续时间过程的有限内存时间传播线性积分预测指标，以使相应的预测内核具有有限的支持。结果表明，具有指数衰减的傅立叶变换的过程在某种弱的意义上是可以预测的，这意味着在未来时代，卷积积分可以通过过去的因果卷积近似。对于给定的预测范围，预测因子基于加权$ L_2 $ - 空间中周期性指数（复杂的正弦）的多项式近似。
The paper presents transfer functions for limited memory time-invariant linear integral predictors for continuous time processes such that the corresponding predicting kernels have bounded support. It is shown that processes with exponentially decaying Fourier transforms are predictable with these predictors in some weak sense, meaning that convolution integrals over the future times can be approximated by causal convolutions over past times. For a given predicting horizon, the predictors are based on polynomial approximation of a periodic exponent (complex sinusoid) in a weighted $L_2$-space.
