A NOVEL R/S FRACTAL ANALYSIS AND WAVELET ENTROPY CHARACTERIZATION APPROACH FOR ROBUST FORECASTING BASED ON SELF-SIMILAR TIME SERIES MODELING

DOI10.1142/S0218348X20400320zbMath1482.91200OpenAlexW3021822319MaRDI QIDQ5025602

Yeliz Karaca, Dumitru Baleanu

Publication date: 2 February 2022

Published in: Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218348x20400320

zbMATH Keywords

self-similarityforecastingHurst exponentfinancial time seriesartificial neural networkwavelet entropy\((R/S)\) fractal analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Artificial neural networks and deep learning (68T07) Fractals (28A80) Financial markets (91G15)

Related Items (2)

SECONDARY PULMONARY TUBERCULOSIS RECOGNITION BY ROTATION ANGLE VECTOR GRID-BASED FRACTIONAL FOURIER ENTROPY ⋮ QUANTUM STATISTICAL PERSPECTIVE TO EXAMINE THE SOURCE CHAOS FRACTION THROUGH BOSON FEMTOSCOPY

Uses Software

• Matlab

Cites Work

• Unnamed Item
• Entropy evolution and uncertainty estimation with dynamical systems
• Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG
• Computational methods for data analysis
• Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates
• Ordinal regression based on learning vector quantization
• FRACTAL GEOMETRY OF FINANCIAL TIME SERIES
• Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

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