Abstract:The paper presents two missing data filling methods which can be applied to time series forecasting. The basic idea of the proposed methods is that usually, the forecasted parameter (in this case PM 2.5 air pollutant concentration) is dependent on some related parameters that influence its value. When the parameter time series have missing data due to various reasons (e.g. faulty measurement instruments), the time series of other parameters (if available) can be used to fill in the missing values. One method i… Show more
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