Boosting for real and functional samples: an application to an environmental problem
| Autor: | W. González Manteiga, B. M. Fernández de Castro |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Environmental Engineering
Boosting (machine learning) Artificial neural network Lag Computational intelligence Autoregressive model Econometrics Environmental Chemistry Gradient boosting Safety Risk Reliability and Quality Algorithm Physics::Atmospheric and Oceanic Physics General Environmental Science Water Science and Technology Mathematics |
| Zdroj: | Stochastic Environmental Research and Risk Assessment. 22:27-37 |
| ISSN: | 1436-3259 1436-3240 |
| DOI: | 10.1007/s00477-007-0156-8 |
| Popis: | In this paper, boosting techniques are given in order to forecast SO2 levels near a power plant. We use boosting with neural networks to forecast real values of SO2 concentration. Then, the data are considered as a time series of curves. Assuming a lag one dependence, the predictions are computed using the functional kernel and the linear autoregressive Hilbertian model. Boosting techniques are developed for those functional models. We compare results of functional boosting with different starting points and iterate models. We carry out the estimation, in real and functional cases, with the information given by a historical matrix, which is a subsample that emphasizes relevant SO2 values. |
| Databáze: | OpenAIRE |
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