A Fast PM2.5 Forecast Approach Based on Time-Series Data Analysis, Regression and Regularization

The problem of air pollution has become a serious issue in developed as well as developing countries. Unfortunately, most of the current solutions are not very effective and this makes it important to have an efficient early warning system for monitoring and forecasting air quality. Our main focus is to build a real-time forecasting system with high accuracy, and deploy it in Taiwan. In this paper, we propose a forecast method called Adaptive Iterative Forecast (AIF), which can predict the value of PM2.5 for the next few hours (by linear programming, normalization and time-series) based on the trend of historical data. The goal of this research is to develop an efficient and accurate forecast model. Through various comparative analyses, we have proved that our model can achieve significant results. Based on the results, we have also built a real-time forecasting system which allows the users to stay aware of the air quality and plan their day to day life.