Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series

2025/05/20 by Duda, Jarek
#Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2506.05354

Abstract

Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time t: optimizing F_t=∑_τ

Citations

• Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series
• Adaptive exponential power distribution with moving estimator for nonstationary time series
• Parametric context adaptive Laplace distribution for multimedia compression
• Exploiting statistical dependencies of time series with hierarchical correlation reconstruction
• Multifractal detrended fluctuation analysis of nonstationary time series
• Generalized autoregressive conditional heteroskedasticity

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