Multi-regression Forecast in Stochastic Chaos

Alexander Musaev (), Andrey Makshanov () and Dmitry Grigoriev ()
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Alexander Musaev: St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences
Andrey Makshanov: Admiral Makarov State University of Maritime and Inland Shipping
Dmitry Grigoriev: St. Petersburg State University

Computational Economics, 2024, vol. 64, issue 1, No 6, 137-160

Abstract: Abstract This paper addresses the challenge of short-term forecasting for processes modeled as output signals of nonlinear dynamic systems in unstable environments with non-stationary, non-Gaussian interference. Traditional computational forecasting methods are often ineffective for such chaotic processes, which exhibit exponential divergence of trajectories. We propose a solution based on multidimensional correlations with other processes in the same environment. Our main hypothesis, supported by previous research, is that the dynamics of mutual connections have higher inertia than the initial processes. This allows us to form a short-term forecast using modified multidimensional regression analysis techniques.

Keywords: Stochastic chaos; Forecasting; Multi-regression data analysis; Sliding observation window; Multidimensional statistical analysis; Asset management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10440-0

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