An Efficient Data Preprocessing Framework for Time-Series Air Pollution Data

Real-world datasets are often characterised by noise, missing values, inconsistencies, and anomalies, which can adversely affect the performance of data mining and machine learning algorithms. Therefore, data preprocessing is an essential step in ensuring data quality and improving the efficiency and accuracy of the analytical process. The time-series pollution dataset is initially preprocessed to ensure data quality and consistency before applying statistical or machine-learning techniques. The preprocessing begins with the identification and treatment of missing or invalid observations, represented as NaN (Not a Number) values. The paper emphasis on incorporating Appropriate imputation techniques to handle missing observations while preserving the temporal characteristics of the dataset. Also, outliers and anomalous observations are detected and treated using suitable statistical methods. Pollution data exhibit extreme observations due to sudden variations in pollutant concentrations, and their distribution may be left-skewed or right-skewed. Therefore, statistical measures such as the Interquartile Range (IQR) are employed to identify and appropriately handle abnormal observations. These preprocessing procedures help produce a reliable and consistent hourly time-series dataset, thereby improving the quality of subsequent analysis and the accuracy of pollution and AQI prediction models. The paper proposes an efficient Data preprocessing framework for time-series air pollution datasets.