# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 2 Related Work  
### Missing Values, Imputation, and Forecasting  
In data engineering, applications such as air pollution data analysis and prediction and the imputation of missing values are real and inevitable problems [8; 9]. As a result, various ways to impute missing data have been developed. Many research papers have been observed where the missing data were removed, and then the analysis is performed on the remaining data. However, it is always vital to replace missing values with some significant values that may improve the performance of the system. Moreover, if the data analysis is performed without replacing missing values, the quality of the data analysis is contentious. The proposed method in this paper takes care of missing values by implementing iterative imputation. Missing data are always lost in its whole and for all time, but an adequate imputation strategy can help to alleviate the problem as much as possible. Missing data are a significant problem in several scientific fields, especially environmental research [10]. Many univariate methods, such as nearest neighbor imputation, linear imputation, and spline imputation, along with multivariate methods, such as self-organizing map imputation, multilayer perceptron imputation, regression-based imputation, and multivariate nearest neighbor imputation, as well as hybrid methods containing combinations of imputation methods were compared and evaluated, which shows that certain multivariate methods for imputation are better choices [11]. Several factors including the pattern of missing data and the type of missing data influence the appropriate technique for dealing with missing data. Simple imputation methods include missing data imputation by either mean, median of the respective column, or replacing the missing value with the proceeding or succeeding value. The authors in [12] interpolated missing values in environmentally contaminated datasets using a single imputation method termed the site-dependent effect method (SDEM) which provides superior imputation than row-mean imputation. The missing values can be imputed using various regression models, such as multiple linear regression or artificial neural network techniques. In [13], it was concluded that for air pollution prediction, the ANN method performed better than the simple regression method, which provides intuition regarding the use of ANN techniques such as iterative imputation. The vector autoregressive imputation technique (VAR-IM) is a novel approach for imputing missing values in multivariate time series datasets that improves speed and accuracy [14]. If the percentage of missing data is fairly minimal, VAR-IM does not have priority for imputation (less than 10 percent). Out of the various methods used for imputation, singular value decomposition (SVD), the k-nearest neighbor (KNN) method, and the sequential k-nearest neighbor (SKNN) method provide better imputation accuracy for air pollution datasets [15]. In a comprehensive literature survey performed on missing data, it was determined that both the miss forest (iterative imputation method) and k nearest neighbor methods can handle missing values successfully [16]. The missing values were replaced with a linear interpolation method in the preprocessing stage, and then multiple pollutants were predicted using the MS-TCN model, which performed better compared to other baseline models [17]. The state-of-the-art method to impute multivariate data via chained equations [18] and iterative imputation, miss forest, and deep learning approaches [19; 20] was used to impute missing data in air quality datasets. In order to deal with missing data in the air quality datasets, multiple data mining techniques [21; 22] as well as statistical techniques [23; 24] were implemented for appropriate imputation.