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

• URL: http://arxiv.org/abs/2211.09814v1
• Date: Wed, 16 Nov 2022 09:37:08 GMT
• Title: Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM
• Authors: Iryna Talamanova, Sabri Pllana
• Abstract summary: We use a data-driven approach to predict air quality based on historical data. Considering prediction accuracy and time complexity, our experiments reveal that for short-term air pollution prediction ES performs better than ARIMA and LSTM.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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