Imputation Analysis for Time Series Air Quality (PM10) Data Set: A Comparison of Several Methods

• Published in: Journal of Physics: Conference Series
• Main Author: Shaadan N.; Rahim N.A.M.
• Format: Conference paper
• Language: English
• Published: Institute of Physics Publishing 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076091384&doi=10.1088%2f1742-6596%2f1366%2f1%2f012107&partnerID=40&md5=4e71203f3ced4d9c6ae703236a27c4d8

Good quality data is important to guarantee for the best quality results of research analysis. However, the quality of the data often being impacted by the existence of missing values that bring bad implication on the accuracy of analysis and subsequently lead to biased results. In air quality data set, missing values problem often caused by various reasons, for example machine malfunction and errors, computer system crashes, human error and insufficient sampling used. In the case for time series modelling, complete series of data is very important to enable for the model construction. This paper aims to highlight a systematic statistical procedure and analysis on how to investigate the performance of several missing values imputation methods to solve for the problem of missing value existence when data are time series. The knowledge could help researchers to implement a comprehensive procedure in deciding a type of imputation method that suits with their data. A case study was conducted using real data set from Shah Alam air quality monitoring station. The results have shown that the missing data at the monitoring station is completely at random (MCAR). Among six imputation methods compared and based on the performance of indicators such as RMSE, MAE, AI and R2 it is shown that imputation using Kalman Filter using ARIMA model is the best appropriate method for the data set. © Published under licence by IOP Publishing Ltd.