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

TL;DR

This paper compares ES, ARIMA, and LSTM methods for short-term air quality prediction, finding that ES outperforms the others in accuracy and efficiency for urban pollution forecasting.

Contribution

It provides a comparative analysis of three popular time series prediction methods specifically for short-term air quality forecasting.

Findings

ES outperforms ARIMA and LSTM in prediction accuracy

ES has lower time complexity than ARIMA and LSTM

Short-term air quality prediction benefits from data-driven approaches

Abstract

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.