A new procedure in stock market forecasting based on fuzzy random auto-regression time series model

DOI10.1016/j.ins.2018.02.016zbMath1448.91281OpenAlexW2791844767MaRDI QIDQ2198011

Riswan Efendi, Nureize Arbaiy, Mustafa Mat Deris

Publication date: 8 September 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2018.02.016

zbMATH Keywords

stock marketfuzzy random variableauto-regression modelleft-right spreadlow-high procedure

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Inference from stochastic processes and fuzziness (62M86) Financial markets (91G15)

Related Items (5)

Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling ⋮ An incremental-hybrid-Yager's entropy model for dynamic portfolio selection with fuzzy variable ⋮ A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters ⋮ \(\mathcal{L}_p\)-norm of the LMS algorithm for bilinear forms with \(\alpha\)-stable processes ⋮ The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm

Uses Software

• itsmr

Cites Work

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