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

doi:10.12302/j.issn.1000-2006.202205005

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (3): 268-274.doi: 10.12302/j.issn.1000-2006.202205005

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Improved time series models based on EMD and CatBoost algorithms: taking PM_2.5 prediction of Dalian City as an example

ZHAO Lingxiao¹^,²(), LI Zhiyang³, QU Leilei⁴^,^*()

1. College of Marine and Civil Engineering, Dalian Ocean University, Dalian 116023, China
2. Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
3. College of Civil Engineering, Chongqing University, Chongqing 400044, China
4. College of Information Engineering, Dalian Ocean University, Dalian 116023, China

Received:2022-05-04 Revised:2023-08-11 Online:2024-05-30 Published:2024-06-14

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Abstract

Abstract:

【Objective】 The study aims to address the problem of low accuracy in traditional PM_2.5 concentration time series prediction, and to reduce the impact of nonlinearity, high noise, instability and volatility on the prediction of PM_2.5 time series, to predict PM_2.5 concentration more accurately. 【Method】 The haze PM_2.5 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 PM_2.5 time series models. Compared with the traditional time series models, the EMD-CatBoost-ARIMA model has higher performance in PM_2.5 concentration prediction.

Key words: PM_2.5 concentration, empirical modal decomposition(EDM), time series model, hybrid model, CatBoost algorithm, machine learning, Dalian City

CLC Number:

X513

ZHAO Lingxiao, LI Zhiyang, QU Leilei. Improved time series models based on EMD and CatBoost algorithms: taking PM_2.5 prediction of Dalian City as an example[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(3): 268-274.

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DF检验参数 Dickey-Fuller test parameter	值 value
统计量test statistic	-5.67
P	8.93×10^-7
滞后项lags used	25.0
观测数据量number of observations used	2.93×10³
1%关键值1%critical value	-3.43
5%关键值5%critical value	-2.86
10%关键值10%critical value	-2.57
数据类别data type	float64

分类 classification	模型 model	均方根误差 RMSE	平均绝对值误差 MAE	希尔不等系数 TIC
原始时间序列模型 original time series model	AR	16.577 6	10.389 4	0.325 9
原始时间序列模型 original time series model	ARIMA	15.733 0	10.970 4	0.272 4
EMD混合模型 EMD hybrid model	EMD-AR	13.895 7	9.458 1	0.248 4
	EMD-ARIMA	13.881 3	9.419 3	0.246 5
	EMD-ARIMA-AR	13.880 9	9.463 4	0.248 0
CatBoost综合模型 CatBoost integrated model	EMD-CatBoost-AR	13.143 2	9.059 8	0.231 1
CatBoost综合模型 CatBoost integrated model	EMD-CatBoost-ARIMA	13.135 3	9.060 8	0.230 8

Improved time series models based on EMD and CatBoost algorithms: taking PM_2.5 prediction of Dalian City as an example

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