000			01966nam a22002537a 4500
005			20250625163918.0
008			250625b |||||||| |||| 00| 0 eng d
020			_a9783111325330
040			_aCSL _cCSL
041			_2eng _aeng
084			_aB2811 R4 _qCSL
100			_aLuz, Maksym _eauthor. _9814629
245			_aNon-Stationary Stochastic Processes Estimation _b: Vector Stationary Increments, Periodically Stationary Multi-Seasonal Increments
260			_aBoston : _bDe Gruyter, _c2024.
300			_axviii, 292p. _b: ill. _c; 24 cm.
500			_aIncludes bibliography and index.
520			_aThe problem of forecasting future values of economic and physical processes, the problem of restoring lost information, cleaning signals or other data observations from noise, is magnified in an information-laden word. Methods of stochastic processes estimation depend on two main factors.The first factor is construction of a model of the process being investigated. The second factor is the available information about the structure of the process under consideration. In this book, we propose results of the investigation of the problem of mean square optimal estimation (extrapolation, interpolation, and filtering) of linear functionals depending on unobserved values of stochastic sequences and processeswith periodically stationary and long memory multiplicative seasonal increments.Formulas for calculating the mean square errors and the spectral characteristics of the optimal estimates of the functionals are derived in the case of spectral certainty, where spectral structure of the considered sequences and processes are exactly known.
650			_aStochastic processes. _9417726
650			_a Extrapolation _9814630
650			_aInterpolation. _9716626
650			_a Periodic processes. _9814631
700			_aMoklyachuk, Mikhail _eco-author. _9814632
942			_2CC _cTEXL _hB2811 R4 _n0
999			_c1432963 _d1432963