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 is based on an artificial neural network that has as input the values of the other related parameters measured at time t and as output the value of the missing value of the forecasted parameter at time t. The other method is Holt-Winters which uses as inputs previous values of the forecasted parameter. These methods are proper for cases with larger gaps in the time series (more than several days). These filling methods are compared in terms of statistical indicators (e.g. RMSE). Also, a comparative study was performed for PM 2.5 forecasting accuracy analysis with two forecasting methods: a feed forward artificial neural network and Holt-Winters. KEY WORDS PM 2.5 forecasting, modelling missing data in PM 2.5 time series, artificial neural network, Holt-Winters method.
Artificial intelligence based prediction models provide good air pollution forecasters, proper to real time forecasting systems. Among them, artificial neural networks are the most used ones, being universal approximators. Usually, the identification of the best neural model (i.e. most accurate one) is based on experiments and results of time series analysis. The paper focuses on time series analysis for particulate matter (PM) air pollutant prediction with artificial neural networks in the Ploiesti city. Two types of neural models were used: feed forward and radial basis function. For each model we have experimented several architectures in order to identify the most accurate one in terms of root mean square error and average square error. The experimental datasets include five time series with concentration measurements of five air pollutants, PM10, CO, NO 2 , NO x , and SO 2 in the period 2008-2012 at PH-6 air quality monitoring station from the Ploiesti city. KEY WORDSTime series analysis, artificial intelligence, air pollution, particulate matter prediction, artificial neural networks.
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