Abstract
The paper analysed CO concentration monitoring data in apron environment to explore the influence factors, and proposed the additive model for CO considering the influence of internal and external factors. Linear interpolation filling the missing values could effectively solve 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 regressions for CO was reconstructed based on linear interpolation filling. The error analysis showed that the accuracy of CO was improved. The prediction effect was also improved by considering the interaction effect.
Published Version
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