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Air pollution is a worldwide issue that affects the lives of many people inurban areas. It is considered that the air pollution may lead to heart and lungdiseases. A careful and timely forecast of the air quality could help to reducethe exposure risk for affected people. In this paper, we use a data-drivenapproach to predict air quality based on historical data. We compare threepopular methods for time series prediction: Exponential Smoothing (ES),Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory(LSTM). Considering prediction accuracy and time complexity, our experimentsreveal that for short-term air pollution prediction ES performs better thanARIMA and LSTM.

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