Multi-step prediction of time series with random missing data

DOI10.1016/j.apm.2013.11.029zbMath1428.62416OpenAlexW2012483024MaRDI QIDQ1629767

Yaonan Wang, Jianxu Mao, Zhaoping Du, Xuedong Wu, Chun-Hua Li

Publication date: 12 December 2018

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2013.11.029

zbMATH Keywords

nonlinear filtersmulti-step predictionmultilayer perceptron ANNrandom missing data

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20)

Related Items (1)

Neural network stochastic differential equation models with applications to financial data forecasting

Cites Work

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• Extended and unscented Kalman filtering based feedforward neural networks for time series prediction
• Maximum likelihood estimation for dynamic factor models with missing data
• Forecasting time series with missing data using Holt's model
• Robust diagnosis of discrete event systems against intermittent loss of observations
• Particle-filter-based estimation and prediction of chaotic states
• Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization
• Oscillation and Chaos in Physiological Control Systems
• Gaussian particle filtering

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