Optimal statistical model for forecasting air quality data

被引：0
|
作者：
Abdollahian, M [1 ]
Foroghi, R [1 ]
机构：
[1] RMIT Univ, Sch Math & Geospatial Sci, Melbourne, Vic, Australia
来源：
METMBS '04: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICS AND ENGINEERING TECHNIQUES IN MEDICINE AND BIOLOGICAL SCIENCES | 2004年
关键词：
univariate time series; ARMA; (p; q); multi linear regression;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 MADE = 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.
引用
收藏
页码：436 / 442
页数：7
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