Denoising Deterministic Time Series
  This paper is concerned with the problem of recovering a finite, deterministic time series from observations that are corrupted by additive, independent noise. A distinctive feature of this problem is that the available data exhibit long-range dependence and, as a consequence, existing statistical theory and methods are not readily applicable. This paper gives an analysis of the denoising problem that extends recent work of Lalley, but begins from first principles. Both positive and negative results are established. The positive results show that denoising is possible under somewhat restrictive conditions on the additive noise. The negative results show that, under more general conditions on the noise, no procedure can recover the underlying deterministic series. 
Steven P. Lalley and Andrew B. Nobel