We deal with the problem of optimal estimation of the linear functionals constructed from the missed values of a continuous time stochastic process $ξ(t)$ with periodically stationary increments at points $t\in[0;(N+1)T]$ based on observations of this process with periodically stationary noise. To solve the problem, a sequence of stochastic functions $
\{ξ^{(d)}_j(u)=ξ^{(d)}_j(u+jT,τ),\,\, u\in [0,T), j\in\mathbb Z\}. $ is constructed. It forms a $L_2([0,T);H)$-valued stationary increment sequence $\{ξ^{(d)}_j,j\in\mathbb Z\}$ or corresponding to it an (infinite dimensional) vector stationary increment sequence $\{\vecξ^{(d)}_j=(ξ^{(d)}_{kj}, k=1,2,\dots)^{\top}, j\in\mathbb Z\}$. In the case of a known spectral density, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal estimates of the functionals. Formulas determining the least favorable spectral densities and the minimax (robust) spectral characteristics of the optimal linear estimates of functionals are derived in the case where the sets of admissible spectral densities are given.
Robust Forecasting of Sequences with Periodically Stationary Long Memory Multiplicative Seasonal Increments Observed with Noise and Cointegrated Sequences
BNP Paribas Cardif（法国巴黎银行康菲）；Taras Shevchenko National University of Kyiv（基辅塔拉斯舍甫琴科国立大学）
