Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market

DOI10.1080/00949655.2011.630000zbMath1431.62486OpenAlexW1984650248MaRDI QIDQ2862356

Erol Eğrioğlu, Cagdas Hakan Aladag, Vedide R. Uslu, Ufuk Yolcu

Publication date: 15 November 2013

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2011.630000

zbMATH Keywords

defuzzificationfuzzy c-means clusteringfeed-forward neural networksfuzzificationfuzzy time seriesfuzzy relationship

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)

Related Items (8)

A new high order fuzzy ARMA time series forecasting method by using neural networks to define fuzzy relations ⋮ Evolving possibilistic fuzzy modelling ⋮ A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data ⋮ A hybrid fuzzy time series approach based on fuzzy clustering and artificial neural network with single multiplicative neuron model ⋮ First-order ARMA type fuzzy time series method based on fuzzy logic relation tables ⋮ An ARMA type fuzzy time series forecasting method based on particle swarm optimization ⋮ High order fuzzy time series forecasting method based on an intersection operation ⋮ A two-step machine learning approach to predict S&P 500 bubbles

Cites Work

• Fuzzy time series and its models
• A FCM-based deterministic forecasting model for fuzzy time series
• FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES
• Forecasting enrollments using high-order fuzzy time series and genetic algorithms
• A method for the solution of certain non-linear problems in least squares
• Effective lengths of intervals to improve forecasting in fuzzy time series

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