WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data
Jintu Borah Tanujit Chakraborty Md. Shahrul Md. Nadzir Mylene G. Cayetano Shubhankar Majumdar
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.