Modeling and forecasting financial time series with ordered fuzzy candlesticks

DOI10.1016/j.ins.2014.03.026zbMath1414.91425OpenAlexW2059410401MaRDI QIDQ726398

T. Burczynski, Adam Marszałek

Publication date: 8 July 2016

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

Full work available at URL: https://doi.org/10.1016/j.ins.2014.03.026

zbMATH Keywords

financial time seriesstock returndirectional predictabilityfuzzy autoregressiveordered fuzzy number

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Inference from stochastic processes and fuzziness (62M86)

Related Items (11)

The modified fuzzy mortality model based on the algebra of ordered fuzzy numbers ⋮ Generalized exponential autoregressive models for nonlinear time series: stationarity, estimation and applications ⋮ The use of ordered fuzzy numbers for modelling changes in dynamic processes ⋮ Introduction to Fuzzy Sets ⋮ Introduction to Fuzzy Systems ⋮ Ordered Fuzzy Numbers: Definitions and Operations ⋮ Two Approaches to Fuzzy Implication ⋮ Ordered Fuzzy Candlesticks ⋮ Fuzzy Numbers Applied to a Heat Furnace Control ⋮ Fuzzy risk analysis for a production system based on the Nagel point of a triangle ⋮ Ordered fuzzy random variables: definition and the concept of normality

Uses Software

• FinTS

Cites Work

• The concept of a linguistic variable and its application to approximate reasoning. I
• Least-squares estimates in fuzzy regression analysis.
• Analysis of Financial Time Series
• Computational aspects of fuzzy arithmetics based on Archimedean t-norms
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