Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions

DOI10.1016/j.chaos.2016.03.037zbMath1360.62468OpenAlexW2323200699MaRDI QIDQ528326

Joon S. Lim, Soo H. Chai

Publication date: 12 May 2017

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2016.03.037

zbMATH Keywords

neuro-fuzzy networkchaotic time series predictiontime delay coordinate embedding

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Statistical methods; risk measures (91G70) Financial applications of other theories (91G80)

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Uses Software

• MINITAB

Cites Work

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• Recognizing business cycle turning points by means of a neural network
• Practical method for determining the minimum embedding dimension of a scalar time series
• Independent coordinates for strange attractors from mutual information
• Fuzzy identification of systems and its applications to modeling and control

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