WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data

arXiv:2404.05482v1 Announce Type: new
Abstract: 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 …

abstract air quality architecture arxiv control cs.lg data development forecast forecasting health novel planning pollution public public health quality quality data real-time regional sustainable sustainable development type urban urban planning wavelet