Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM
This work addresses air pollution forecasting to reduce health risks in urban areas, but it is incremental as it compares existing methods without introducing new ones.
The paper tackled short-term air quality prediction by comparing Exponential Smoothing (ES), ARIMA, and LSTM methods, finding that ES performed better in accuracy and time complexity for this task.
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.