WHY FARIMA MODELS ARE BRITTLE

DOI10.1142/S0218348X13500126zbMath1281.60035arXiv1203.6140OpenAlexW2963127779MaRDI QIDQ2865149

Anders Gorst-Rasmussen, Darryl Veitch, A. Gefferth

Publication date: 28 November 2013

Published in: Fractals (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1203.6140

zbMATH Keywords

self-similaritylong-range dependenceHurst parameterfractional Gaussian noiseFARIMAfractionally differenced process

Mathematics Subject Classification ID

Fractional processes, including fractional Brownian motion (60G22) General second-order stochastic processes (60G12) Self-similar stochastic processes (60G18)

Related Items (1)

An approximate fractional Gaussian noise model with \(\mathcal{O}(n)\) computational cost

Cites Work

• An extension of a logarithmic form of Cramér's ruin theorem to some FARIMA and related processes
• Semiparametric analysis of long-memory time series
• ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY
• Long-range Dependence: Revisiting Aggregation with Wavelets
• Fractional differencing
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
• Fitting long-memory models by generalized linear regression
• RATE OPTIMAL SEMIPARAMETRIC ESTIMATION OF THE MEMORY PARAMETER OF THE GAUSSIAN TIME SERIES WITH LONG‐RANGE DEPENDENCE
• The nature of discrete second-order self-similarity
• An exponential model for the spectrum of a scalar time series

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