TY - JOUR

T1 - Multi-regression Forecast in Stochastic Chaos

AU - Мусаев, Александр Азерович

AU - Макшанов, Андрей

AU - Григорьев, Дмитрий Алексеевич

PY - 2023/8/4

Y1 - 2023/8/4

N2 - 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.

AB - 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.

KW - Asset management

KW - Forecasting

KW - Multi-regression data analysis

KW - Multidimensional statistical analysis

KW - Sliding observation window

KW - Stochastic chaos

UR - https://www.mendeley.com/catalogue/a5c390ef-98ca-382a-b0fd-126db4a49491/

U2 - 10.1007/s10614-023-10440-0

DO - 10.1007/s10614-023-10440-0

M3 - Article

JO - Computational Economics

JF - Computational Economics

SN - 0927-7099

ER -