Improved time series models based on EMD and CatBoost algorithms: taking PM2.5 prediction of Dalian City as an example

ZHAO Lingxiao, LI Zhiyang, QU Leilei

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (3) : 268-274.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (3) : 268-274. DOI: 10.12302/j.issn.1000-2006.202205005

Improved time series models based on EMD and CatBoost algorithms: taking PM2.5 prediction of Dalian City as an example

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Abstract

【Objective】 The study aims to address the problem of low accuracy in traditional PM2.5 concentration time series prediction, and to reduce the impact of nonlinearity, high noise, instability and volatility on the prediction of PM2.5 time series, to predict PM2.5 concentration more accurately. 【Method】 The haze PM2.5 data of Dalian City from January 1, 2014 to January 31, 2022 was used as an example. In this study, a hybrid machine learning time series model with the combination of empirical modal decomposition (EMD), classification boosting (CatBoost) and autoregressive integrated moving average model (ARIMA) was proposed. It was compared with the traditional autoregressive model (AR), ARIMA and the hybrid model with only the EMD method. 【Result】 The hybrid model EMD-CatBoost-ARIMA improved the root mean square error (RMSE) of the original sequence by 20.76%, the mean absolute error (MAE) by 17.40%, and the theil inequality coefficient (TIC) by 29.17%. 【Conclusion】 For reconstructed sequences with high entropy values, the EMD decomposition method and CatBoost algorithm can significantly improve the prediction performance of PM2.5 time series models. Compared with the traditional time series models, the EMD-CatBoost-ARIMA model has higher performance in PM2.5 concentration prediction.

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PM2.5 concentration / empirical modal decomposition(EDM) / time series model / hybrid model / CatBoost algorithm / machine learning / Dalian City

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ZHAO Lingxiao , LI Zhiyang , QU Leilei. Improved time series models based on EMD and CatBoost algorithms: taking PM2.5 prediction of Dalian City as an example[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2024, 48(3): 268-274 https://doi.org/10.12302/j.issn.1000-2006.202205005

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