  Air pollution is a worldwide issue that affects the lives of many people in
urban areas. It is considered that the air pollution may lead to heart and lung
diseases. A careful and timely forecast of the air quality could help to reduce
the exposure risk for affected people. In this paper, we use a data-driven
approach to predict air quality based on historical data. We compare three
popular 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 experiments
reveal that for short-term air pollution prediction ES performs better than
ARIMA and LSTM.
