Prediction of stochastic processes using self-tuning principles

DOI10.1080/00207728708963973zbMath0617.93067OpenAlexW1973039781MaRDI QIDQ3754539

P. P. Kanjilal, David W. Clarke

Publication date: 1987

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207728708963973

zbMATH Keywords

self-tuningARIMAX process modelconstrained minimum-variance predictionGeneralized cost functions

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Filtering in stochastic control theory (93E11) Adaptive control/observation systems (93C40) Identification in stochastic control theory (93E12) Analysis of variance and covariance (ANOVA) (62J10)

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Cites Work

• Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach
• Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes
• A generalized LQG approach to self-tuning control Part I. Aspects of design
• A self-tuning predictor
• Time-Series Analysis and Forecasting: An Update and Evaluation
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