Identification environment and robust forecasting for nonlinear time series

DOI10.1007/BF01299328zbMath0789.62074OpenAlexW2086479460MaRDI QIDQ1318308

Berlin Wu

Publication date: 16 June 1994

Published in: Computational Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01299328

zbMATH Keywords

predictionneural networksimulation studynonlinear time seriesexpert systemLagrange multiplier testbilinear time seriesbackpropagation approximationidentification environmentintegrated dynamic designnew test designrobust forecastingrobust forecasting designself- organizational properties

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

An investigation of neural networks for linear time-series forecasting ⋮ FORECASTING FUZZY TIME SERIES ON A HEURISTIC HIGH-ORDER MODEL

Cites Work

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• An introduction to bispectral analysis and bilinear time series models
• Bilinear control processes. With applications to engineering, ecology, and medicine
• Testing linearity against smooth transition autoregressive models
• TESTING FOR GAUSSIANITY AND LINEARITY OF A STATIONARY TIME SERIES
• Testing and Modeling Threshold Autoregressive Processes
• On the identification problem for bilinear time series models
• Approximation by superpositions of a sigmoidal function

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