Non-Stationary Stochastic Processes Estimation

Vector Stationary Increments, Periodically Stationary Multi-Seasonal Increments

Maksym Luz, Mikhail Moklyachuk

• Publisher: De Gruyter
• Year: 2024
• Print ISBN: 9783111325330
• eBook ISBN: 9783111326252
• Edition: 1
• Topic: Economics
• Subtopic: Business Mathematics
• Index: Economics

The 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 processes

with 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.

In the case where spectral densities of the sequences and processes are not known exactly while some sets of admissible spectral densities are given, we apply the minimax-robust method of estimation.

Table of contents

• Title Page
• Copyright
• Contents
• 1 Periodically stationary multi-seasonal increments of stochastic sequences
• 2 Extrapolation of sequences with periodically stationary increments
• 3 Extrapolation of sequences with periodically stationary increments observed with noise
• 4 Interpolation of sequences with periodically stationary increments observed with or without noise
• 5 Filtering of sequences with periodically stationary increments
• 6 Continuous time stochastic processes with periodically correlated increments
• 7 Extrapolation of processes with periodically correlated increments
• 8 Extrapolation of processes with periodically correlated increments observed with noise
• 9 Interpolation of processes with periodically correlated increments observed with or without noise
• 10 Filtering of processes with periodically correlated increments
• 11 Filtering problem when signal and noise have periodically correlated increments
• Subject Index