WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data
<|reference_start|>WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data: 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.<|reference_end|>
@article{borah2024wavecatboost,
    title={WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data},
    author={Jintu Borah, Tanujit Chakraborty, Md. Shahrul Md. Nadzir, Mylene G.
  Cayetano, Shubhankar Majumdar},
    journal={arXiv preprint arXiv:2404.05482},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05482},
    primaryClass={cs.LG}
}