Abdollahian, M. ; Foroughi, Roya (2005)
Regression Analysis of Ozone Data.
IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The objective of this paper is to apply multiple regression techniques to ozone data in order to predict next day ozone levels. Examination of several possible contributing factors, showed that Wind speed, Mixing height where the complex chemical reactions that produce ozone take place, current and predicted next day temperatures and current ozone concentration are influential on the next day ozone concentration levels. These variables were then considered as explanatory variables in regression models. Subsequently, diagnostics tests and statistics including R-square residual analysis and Durbin-Watson Statistic were applied in order to select the best fitted model and finally the best prediction model was found using Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) as predictive criteria.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2005 |
| Autor(en): | Abdollahian, M. ; Foroughi, Roya |
| Art des Eintrags: | Bibliographie |
| Titel: | Regression Analysis of Ozone Data |
| Sprache: | Englisch |
| Publikationsjahr: | 2005 |
| Ort: | Los Alamitos, Calif. [u.a.] |
| Verlag: | IEEE Computer Society |
| Veranstaltungstitel: | IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I |
| Kurzbeschreibung (Abstract): | The objective of this paper is to apply multiple regression techniques to ozone data in order to predict next day ozone levels. Examination of several possible contributing factors, showed that Wind speed, Mixing height where the complex chemical reactions that produce ozone take place, current and predicted next day temperatures and current ozone concentration are influential on the next day ozone concentration levels. These variables were then considered as explanatory variables in regression models. Subsequently, diagnostics tests and statistics including R-square residual analysis and Durbin-Watson Statistic were applied in order to select the best fitted model and finally the best prediction model was found using Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) as predictive criteria. |
| Freie Schlagworte: | Regression analysis, Statistics, Forecasting theory |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Apr 2018 09:04 |
| Letzte Änderung: | 23 Apr 2020 07:57 |
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