论文信息 - On Nonlinear Processing of Air Pollution Data

On Nonlinear Processing of Air Pollution Data

Three methods — DVS plots, attractor reconstruction, and variance analysis of delay vectors — for detecting nonlinearities in time series are compared on an air pollution dataset. For rigour each method is also used on a surrogate dataset, based on a high-order linear fit to the original data. Finally, a comparison of a standard linear analysis to a neural network model analysis of the air pollution dataset is provided.

Gavin C. Cawley | Danilo P. Mandic | Igor R. Krcmar | Rob Foxall | Stephen Dorling

[1] Danilo P. Mandic,et al. On Predictability of Atmospheric Pollution Time Series , 2001 .

[2] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .

[3] D. P. Mandic,et al. NNGD algorithm for neural adaptive filters , 2000 .

[4] Andreas S. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[5] Kenji Nakayama,et al. A Hybrid Nonlinear Predictor: Analysis of Learning Process and Predictability for Noisy Time Series (Special Section on Digital Signal Processing) , 1999 .

[6] F. Becker,et al. Detecting nonlinearities in time series of machining processes , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[7] Danilo P. Mandic,et al. Relating the Slope of the Activation Function and the Learning Rate Within a Recurrent Neural Network , 1999, Neural Computation.