Vladimir  Alekseevich Golovkov, 
    Models of Stohastic Processes and Their use in Optimal Linear Inteprolation and Forecasting,
    International Journal of System Signal Control and Engineering Application,
    Volume 10,Issue 4,
    2017,
    Pages 113-116,
    ISSN 1997-5422,
    ijssceapp.2017.113.116,
    (https://makhillpublications.co/view-article.php?doi=ijssceapp.2017.113.116)
    Abstract: In this study, I consider models of stochastic
process correlation functions and, by way of numerical
calculation, prove that the efficiency of optimal linear
interpolation and forecasting is determined by the existing
highest derivative of a stochastic process. I also set out
the results of numerical calculations pertaining to
efficiency assessment of interpolation and forecasting of
finitely differentiable stochastic processes with correlation
functions commonly used in practice for Wiener-Hopf
filtering.
    Keywords: Stochastic process;differentiability;wiener-Hopf filtering;forecasting;interpolation