Estimation of structural changes in nonlinear time series models by using particle filters and genetic programming

DOI10.1002/acs.2578zbMath1348.93257OpenAlexW1565781035MaRDI QIDQ2828580

Shozo Tokinaga, Jianjun Lu

Publication date: 26 October 2016

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.2578

zbMATH Keywords

state estimationgenetic programmingparticle filtersstructural changesnonlinear time series modeling

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Filtering in stochastic control theory (93E11) Approximation methods and heuristics in mathematical programming (90C59) Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10)

Cites Work

• Structural shocks and the comovements between output and interest rates
• Sequential Monte Carlo Methods in Practice
• A sigma point-based resampling algorithm in particle filter
• Implicit dual control based on particle filtering and forward dynamic programming
• Information Sharing in a Long-Term Supply Chain Relationship: The Role of Customer Review Strategy
• The Value of Information Sharing in a Two-Level Supply Chain
• Tests of Equality Between Sets of Coefficients in Two Linear Regressions
• Information Distortion in a Supply Chain: The Bullwhip Effect
• Observer-based approach to fault detection and isolation for nonlinear systems
• Additive Change Detection in Nonlinear Systems With Unknown Change Parameters
• Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures
• An EM Algorithm for Nonlinear State Estimation With Model Uncertainties
• Particle filtering-based fault detection in non-linear stochastic systems
• Gaussian sum particle filtering
• Marginalized particle filters for mixed linear/nonlinear state-space models
• A new paradigm for parameter estimation in system modeling
• EXPLANATORY RULE EXTRACTION BASED ON THE TRAINED NEURAL NETWORK AND THE GENETIC PROGRAMMING
• Model selection by MCMC computation.

This page was built for publication: Estimation of structural changes in nonlinear time series models by using particle filters and genetic programming