Accurate and reliable air quality forecasting is essential for protecting
public health, sustainable development, pollution control, and enhanced urban
planning. This letter presents a novel WaveCatBoost architecture designed to
forecast the real-time concentrations of air pollutants by combining the
maximal overlapping discrete wavelet transform (MODWT) with the CatBoost model.
This hybrid approach efficiently transforms time series into high-frequency and
low-frequency components, thereby extracting signal from noise and improving
prediction accuracy and robustness. Evaluation of two distinct regional
datasets, from the Central Air Pollution Control Board (CPCB) sensor network
and a low-cost air quality sensor system (LAQS), underscores the superior
performance of our proposed methodology in real-time forecasting compared to
the state-of-the-art statistical and deep learning architectures. Moreover, we
employ a conformal prediction strategy to provide probabilistic bands with our
forecasts.

提出了一种新颖的 WaveCatBoost 架构，它通过将最大重叠离散小波变换（MODWT）与 CatBoost 模型相结合，来预测空气污染物的实时浓度。该混合方法可以高效地将时间序列转换为高频和低频成分，从而提取信号、改善预测准确性和稳健性。与最先进的统计学和深度学习架构相比，对来自中央空气污染控制委员会（CPCB）传感器网络和低成本空气质量传感器系统（LAQS）的两个不同地区数据集进行评估，结果显示我们所提出的方法在实时预测方面表现出卓越的性能。此外，我们采用一种遵循预测策略，在我们的预测中提供概率带。

WaveCatBoost 用于区域空气质量数据的概率预测

WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data

This research arises from the need to predict the amount of air pollutants in
meteorological stations. Air pollution depends on the location of the stations
(weather conditions and activities in the surroundings). Frequently, the
surrounding information is not considered in the learning process. This
information is known beforehand in the absence of unobserved weather conditions
and remains constant for the same station. Considering the surrounding
information as side information facilitates the generalization for predicting
pollutants in new stations, leading to a zero-shot regression scenario.
Available methods in zero-shot typically lean towards classification, and are
not easily extensible to regression. This paper proposes two zero-shot methods
for regression. The first method is a similarity based approach that learns
models from features and aggregates them using side information. However,
potential knowledge of the feature models may be lost in the aggregation. The
second method overcomes this drawback by replacing the aggregation procedure
and learning the correspondence between side information and feature-induced
models, instead. Both proposals are compared with a baseline procedure using
artificial datasets, UCI repository communities and crime datasets, and the
pollutants. Both approaches outperform the baseline method, but the parameter
learning approach manifests its superiority over the similarity based method.

通过使用侧信息的相似度和参数学习方法，本文提出了两种用于回归的零样本方法，用于预测气象站的空气污染物数量，并在人工数据集、UCI 存储库社区和犯罪数据集以及污染物方面进行了比较。这两种方法均优于基线方法，但参数学习方法表现出了较高的优越性。