713 research outputs found

    On predictors for band-limited and high-frequency time series

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    Pathwise predictability and predictors for discrete time processes are studied in deterministic setting. It is suggested to approximate convolution sums over future times by convolution sums over past time. It is shown that all band-limited processes are predictable in this sense, as well as high-frequency processes with zero energy at low frequencies. In addition, a process of mixed type still can be predicted if an ideal low-pass filter exists for this process.Comment: 10 pages. arXiv admin note: text overlap with arXiv:0708.034

    Duality and semi-group property for backward parabolic Ito equations

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    We study existence, uniqueness, semi-group property, and a priori estimates for solutions for backward parabolic Ito equations in domains with boundary. We study also duality between forward and backward equations. The semi-group for backward equations is established in the form of some anti-causality. The novelty is that the semi-group property involves the diffusion term that is a part of the solution

    Representation of functionals of Ito processes and their first exit times

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    The representation theorem is obtained for functionals of non-Markov processes and their first exit times from bounded domains. These functionals are represented via solutions of backward parabolic Ito equations. As an example of applications, analogs of forward Kolmogorov equations are derived for conditional probability density functions of Ito processes being killed on the boundary. In addition, a maximum principle and a contraction property are established for SPDEs in bounded domains.Comment: 25 page

    On causal extrapolation of sequences with applications to forecasting

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    The paper suggests a method of extrapolation of notion of one-sided semi-infinite sequences representing traces of two-sided band-limited sequences; this features ensure uniqueness of this extrapolation and possibility to use this for forecasting. This lead to a forecasting method for more general sequences without this feature based on minimization of the mean square error between the observed path and a predicable sequence. These procedure involves calculation of this predictable path; the procedure can be interpreted as causal smoothing. The corresponding smoothed sequences allow unique extrapolations to future times that can be interpreted as optimal forecasts.Comment: arXiv admin note: substantial text overlap with arXiv:1111.670