A Comparative Study of Statistical and Machine Learning Modelling Techniques in Air Pollution Data

Different approaches are being adopted in practice for determining models for given time series. The approaches can be categorized broadly into three, viz., statistical, machine learning and deep learning. Since they differ with respect to their theoretical base, their outcomes also differ. Decision-making based on the values predicted from the time series models seeks the accuracy of the forecast values. This paper studies the effectiveness of the three approaches by comparing the performance of the autoregressive moving average method developed by applying statistical principles, the Facebook Prophet method developed from a Machine Learning approach and the long short-term memory method developed from deep learning. The study is carried out for real data of time series of air quality indices.