论文标题
数据驱动的实时空气质量短期预测：ES，Arima和LSTM的比较
Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM
论文作者
论文摘要
空气污染是一个全球问题，影响了城市地区许多人的生活。据认为，空气污染可能导致心脏和肺部疾病。对空气质量的仔细预测可以帮助减少受影响人的接触风险。在本文中，我们使用数据驱动的方法根据历史数据来预测空气质量。我们比较了时间序列预测的三种流行方法：指数平滑（ES），自动回归整合运动平均值（ARIMA）和长期记忆（LSTM）。考虑到预测准确性和时间复杂性，我们的实验表明，对于短期空气污染，预测的表现要好于Arima和LSTM。
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.
