Abdollahian, Mali ; Foroughi, Roya (2004)
Optimal Statistical Model for Forecasting Air Quality Data.
International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS '04). Las Vegas, USA (21.- 24.06.2004)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The objective of this paper is to apply time series analysis and regression methods to air quality data in order to obtain the optimal statistical model for forecasting. The best estimated model is then used to produce one-step ahead point and interval estimates of future values of the Airborne Particles Index (API) series. API data is analysed using time series analysis, which resulted in an ARMA (2,3) with MAPE = 62. Regression analysis of this data, using temperature, wind speed and today's API, as explanatory variables, results in MAPE=42, which is substantially less than the previous model.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2004 |
| Autor(en): | Abdollahian, Mali ; Foroughi, Roya |
| Art des Eintrags: | Bibliographie |
| Titel: | Optimal Statistical Model for Forecasting Air Quality Data |
| Sprache: | Englisch |
| Publikationsjahr: | 2004 |
| Verlag: | CSREA Press |
| Buchtitel: | Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Scienes |
| Veranstaltungstitel: | International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS '04) |
| Veranstaltungsort: | Las Vegas, USA |
| Veranstaltungsdatum: | 21.- 24.06.2004 |
| Kurzbeschreibung (Abstract): | The objective of this paper is to apply time series analysis and regression methods to air quality data in order to obtain the optimal statistical model for forecasting. The best estimated model is then used to produce one-step ahead point and interval estimates of future values of the Airborne Particles Index (API) series. API data is analysed using time series analysis, which resulted in an ARMA (2,3) with MAPE = 62. Regression analysis of this data, using temperature, wind speed and today's API, as explanatory variables, results in MAPE=42, which is substantially less than the previous model. |
| Freie Schlagworte: | Time series analysis, Regression analysis |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Apr 2018 09:04 |
| Letzte Änderung: | 24 Nov 2022 10:10 |
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