A Comparison of Imputation Methods in Estimating PM2.5 Concentrations Using LSTM Neural Networks
Keywords:
Imputation, PM2.5, Neural Network, LSTMAbstract
Missing data is one of the common problems in data collection for research purposes. It often affects the accuracy and reliability of the data analysis results, and the findings and conclusions from the study. Methods for handling incomplete data are deletion or imputation by replacing missing values with estimates. In this study, we propose imputation methods for PM2.5 concentration prediction using long short-term memory (LSTM) neural networks. There are four approaches, namely mean, linear interpolation (LI), k-nearest neighbors (KNN) and multiple imputation by chained equations (MICE). The root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the accuracy of PM2.5 predictions, which is an indicator to measure the effectiveness of different imputation methods. Each imputed dataset was applied to four LSTM forecast models. It showed that the KNN (k=11) and MICE methods provided the least errors and therefore were the best imputation techniques. Both gave better results compared to Mean and LI for filling in missing observations. Model 2 had the most accurate forecast among all the data imputation methods.
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