1 / 11

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.

Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related