Open AccessAdditive Calibration Model for NO2 Based on Linear Interpolation
Author(s) -
Yuye Xu,
Xu E
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1616/1/012071
Subject(s) - missing data , interpolation (computer graphics) , linear interpolation , linear regression , calibration , linear model , autoregressive integrated moving average , mathematics , general linear model , statistics , multivariate statistics , bayesian multivariate linear regression , econometrics , computer science , artificial intelligence , time series , mathematical analysis , motion (physics) , polynomial
The paper proposed the additive model for NO2 considering the influence of internal and external factors. Linear interpolation filling the missing values could be effectively solved the problem of data missing and improved the effect of the addictive model of ARIMA and multivariate linear regression. The addictive calibration model by ARIMA and Multiple linear regression for NO2 was reconstructed based on linear interpolation filling. The error analysis showed that the accuracy of NO2 was improved. The prediction effect was also improved by considering the interaction effect.