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Optimal Statistical Model for Forecasting Air Quality Data

Abdollahain, M. and Foroughi, Roya (2004):
Optimal Statistical Model for Forecasting Air Quality Data.
CSREA Press, Las Vegas, In: METMBS '04, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2004
Creators: Abdollahain, M. and Foroughi, Roya
Title: Optimal Statistical Model for Forecasting Air Quality Data
Language: English
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.

Publisher: CSREA Press, Las Vegas
Uncontrolled Keywords: Time series analysis, Regression analysis
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: METMBS '04
Date Deposited: 16 Apr 2018 09:04
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