Forecasting the particulate matter (PM) concentration in South Korea has become urgently necessary owing to its strong negative impact on human life. In most statistical or machine learning methods, independent and identically distributed data, for example, a Gaussian distribution, are assumed; however, time series such as air pollution and weather data do not meet this assumption. In this study, the detrended fluctuation analysis and power-law analysis are used in an analysis of the statistical characteristics of air pollution and weather data. Rigorous seasonality adjustment of the air pollution and weather data was performed because of their complex seasonality patterns and the heavy-tailed distribution of data even after deseasonalization. The maximum correntropy criterion for regression (MCCR) loss was applied to multiple models including conventional statistical models and state-of-the-art machine learning models. The results show that the MCCR loss is more appropriate than the conventional mean squared error loss for forecasting extreme values.

由于其对人类生活的强烈负面影响，预测韩国的颗粒物 (PM) 浓度已成为当务之急。在大多数统计或机器学习方法中，假设独立且同分布的数据，例如高斯分布；然而，空气污染和天气数据等时间序列不符合这一假设。本研究采用去趋势波动分析和幂律分析方法对空气污染和天气数据的统计特征进行分析。由于空气污染和天气数据的复杂季节性模式和即使在去季节化后数据的重尾分布，也对其进行了严格的季节性调整。回归（MCCR）损失的最大相关熵标准被应用于多种模型，包括传统的统计模型和最先进的机器学习模型。结果表明，MCCR损失比传统的均方误差损失更适合预测极值。