To enhance the forecasting accuracy for PM_2.5 concentrations, a novel decomposition-ensemble approach with denoising strategy is proposed in this study. This novel approach is an improved approach under the effective “denoising, decomposition, and ensemble” framework, especially for nonlinear and nonstationary features of PM_2.5 concentration data. In our proposed approach, wavelet denoising approach, as a noise elimination tool, is applied to remove the noise from the original data. Then, variational mode decomposition (VMD) is implemented to decompose the denoised data for producing the components. Next, kernel extreme learning machine (KELM) as a popular machine learning algorithm is employed to forecast all extracted components individually. Finally, these forecasted results are aggregated into an ensemble result as the final forecasting. With hourly PM_2.5 concentration data in Xi’an as sample data, the empirical results demonstrate that our proposed hybrid approach significantly performs better than all benchmarks (including single forecasting techniques and similar approaches with other decomposition) in terms of the accuracy. Consequently, the robustness results also indicate that our proposed hybrid approach can be recommended as a promising forecasting tool for capturing and exploring the complicated time series data.

中文翻译：

---

带有降噪策略的分解-集成方法用于PM2.5浓度预测

为了提高PM _2.5浓度的预测精度，提出了一种具有降噪策略的分解-集成方法。这种新颖的方法是在有效的“降噪，分解和集成”框架下的改进方法，尤其是对于PM _2.5的非线性和非平稳特征浓度数据。在我们提出的方法中，小波去噪方法作为一种噪声消除工具，被用于从原始数据中去除噪声。然后，实施变分模式分解（VMD）以分解经去噪的数据以产生分量。接下来，采用内核极限学习机（KELM）作为一种流行的机器学习算法来分别预测所有提取的组件。最后，将这些预测结果汇总为整体结果，作为最终预测。每小时PM _2.5西安市的浓度数据作为样本数据，经验结果表明，我们提出的混合方法在准确性方面明显优于所有基准（包括单一预测技术和类似方法以及其他分解方法）。因此，鲁棒性结果还表明，我们提出的混合方法可以推荐为有前途的预测工具，用于捕获和探索复杂的时间序列数据。