TY  - JOUR
T1  - Models of Stohastic Processes and Their use in Optimal Linear Inteprolation and Forecasting
AU - Alekseevich Golovkov, Vladimir 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 10
IS  - 4
SP  - 113
EP  - 116
PY  - 2017
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2017.113.116
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2017.113.116
KW  - Stochastic process
KW  -differentiability
KW  -wiener-Hopf filtering
KW  -forecasting
KW  -interpolation
AB  - 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.
ER  -