Nonlinear models for ground-level ozone forecasting

DOI10.1007/BF02511489zbMath1145.62394MaRDI QIDQ1766975

Francesco Lisi, Carlo Gaetan, Silvano Bordignon

Publication date: 3 March 2005

Published in: Statistical Methods and Applications (Search for Journal in Brave)

zbMATH Keywords

combination of forecastsground-level ozone forecastingnonlinear time-series models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12)

Uses Software

• R
• S-PLUS
• AdaBoost.MH

Cites Work

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• Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations
• A prediction-error and stepwise-regression estimation algorithm for non-linear systems
• A Review of Nonparametric Time Series Analysis
• Nonlinear Additive Models for Environmental Time Series, With Applications to Ground-Level Ozone Data Analysis
• An asymptotic property of model selection criteria

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