| Summary: | Forecasting of PM<sub>2.5</sub> concentration is a global concern. Evidence has shown that the ambient PM<sub>2.5</sub> concentrations are harmful to human health, climate change, plant species mortality, etc. PM<sub>2.5</sub> concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM<sub>2.5</sub> prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM<sub>2.5</sub> forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM<sub>2.5</sub> forecasting in Delhi, India.
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