Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM

Air pollution is a worldwide issue that affects the lives of many people in urban areas. It considered air may lead to heart and lung diseases. A careful timely forecast quality could help reduce exposure risk for affected people. In this paper, we use data-driven approach predict based on historical data. We compare three popular methods time series prediction: Exponential Smoothing (ES), Auto-Regressive Integrated Moving Average (ARIMA) Long short-term memory (LSTM). Considering prediction accuracy complexity, our experiments reveal ES performs better than ARIMA LSTM.