Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM
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Abstract (arXiv)
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.
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References · 6
- 2018Forecasting Economics and Financial Time Series: ARIMA vs. LSTMcited by 33
- 2015Accelerating DNA Sequence Analysis using Intel Xeon Phicited by 2
- 2017CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phicited by 2
- 2021Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approachcited by 2
- 2018Real-time Air Pollution prediction model based on Spatiotemporal Big datacited by 1
- 2018A Machine Learning Driven IoT Solution for Noise Classification in Smart Citiescited by 1
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