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Presenter: Jun-Yi Wu Authors: Victor R. Prybutok , Junsub Yi, David Mitchell

Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations. Presenter: Jun-Yi Wu Authors: Victor R. Prybutok , Junsub Yi, David Mitchell. 國立雲林科技大學 National Yunlin University of Science and Technology. 2000 ORMS.

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Presenter: Jun-Yi Wu Authors: Victor R. Prybutok , Junsub Yi, David Mitchell

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  1. Comparison of neural network models with ARIMA andregression models for prediction of Houston's daily maximum ozone concentrations Presenter: Jun-Yi Wu Authors: Victor R. Prybutok, Junsub Yi, David Mitchell 國立雲林科技大學 National Yunlin University of Science and Technology 2000 ORMS

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation • The Houston area has been designated a non-attainment area. • This area started a campaign called “ Ozone Alert Day” • It is difficult to predict the daily ozone concentration.

  4. Objective • To develop and comparea NN model for forecasting maximum daily ozone levels in a non-attainment area to regression and ARIMA models.

  5. Methodology BPLMS Dummy variable Ozone level at 9:00 Maximum daily temperature Carbon dioxide Nitric oxide Nitrogen dioxide Oxide of nitrogen Surface wind speed Surface wind direction Daily maximum ozone level (hourly average) • NN model building

  6. Methodology Dummy variable Ozone level at 9:00 Maximum daily temperature Carbon dioxide Nitric oxide Nitrogen dioxide Oxide of nitrogen Surface wind speed Surface wind direction Daily maximum ozone level (hourly average) • Regression model building • The preliminary regression model • The stepwise procedure • The final regression model

  7. Methodology Daily maximum ozone level • ARIMA (p, d, q) model building • Autoregressive Integrated Moving Average • ARIMA(1,0,0) • Simpson and Layton (1983)

  8. Experiments • Data collection • 1 June -30 September (Train) • October 1-10 (Test) • Variable specification • Dummy variable • Ozone level at 9:00 • Maximum daily temperature • Carbon dioxide • Nitric oxide • Nitrogen dioxide • Oxide of nitrogen • Surface wind speed • Surface wind direction • Daily maximum ozone level (hourly average)

  9. Experiments

  10. Conclusion • The results show that the neural network model is superior to the regression and ARIMA models. 10

  11. Comments • Advantage • This paper is easy to read. • Drawback • This paper lack more experiments. • Application • It is possible to predict the time series data. 11

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