Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting

DOI10.3233/IFS-130775zbMATH Open1306.91107OpenAlexW1828120854MaRDI QIDQ5172226

Mehdi Bijari, Mehdi Khashei

Publication date: 13 February 2015

Published in: Journal of Intelligent & Fuzzy Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3233/ifs-130775

zbMATH Keywords

incomplete datafinancial marketstime series forecastingexchange ratefuzzy logic and modelsartificial neural network \((p, d, q)\)

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Economic time series analysis (91B84) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Intrusion detection algorithm based on improved principal component analysis ⋮ Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting ⋮ A Hybrid ARIMA-ANN approach for optimum estimation and forecasting of gasoline consumption

Recommendations

• A new hybrid artificial neural networks and fuzzy regression model for time series forecasting 👍 👎
• Neural network approach to forecasting of quasiperiodic financial time series 👍 👎
• Unnamed Item 👍 👎
• Unnamed Item 👍 👎
• Unnamed Item 👍 👎
• Unnamed Item 👍 👎

This page was built for publication: Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting