Time Series Forecasting using Exponential Smoothing to Predict the Major Atmospheric Pollutants

Air Quality Index (AQI) is most powerful air quality measure for the common people which can judge the air quality within their locality. It can seem like "One Number - One Color - One Description" that tell people to take necessary precaution. Forecasting the value of atmospheric pollutants will always keep one step forward to safeguard for the urban-dwellers. The objective of our research is to predict the major atmospheric pollutants using exponential smoothing. Exponential smoothing is selected because it is easy to implement and also able to incorporate the trend and seasonality in the data. Furthermore, our research goal is not only forecast but also design fog enabled Internet of Things (IoT) system architecture for real-time processing of these pollutant values so that it save network bandwidth and reduce cost for data storage at cloud platform. These forecasted values will determine the green route in the urban city in near future.