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Supplementary material to quot;Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting systemquot;

机译：“使用小波变换和动态时间翘曲来识别CNN模型作为空气质量预测系统的局限性的补充材料”

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Ebrahim Eslami; Yunsoo Choi; Yannic Lops; Alqamah Sayeed; Ahmed Khan Salman;
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1. Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system [J] . Eslami Ebrahim, Choi Yunsoo, Lops Yannic, Geoscientific Model Development . 2020,第12期

机译：使用小波变换和动态时间翘曲来确定CNN模型作为空气质量预测系统的限制
2. Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system [J] . Eslami Ebrahim, Choi Yunsoo, Lops Yannic, Geoscientific Model Development Discussions . 2020,第12期

机译：使用小波变换和动态时间翘曲来识别CNN模型作为空气质量预测系统的局限性
3. A step toward the practical stratigraphic automatic correlation of well logs using continuous wavelet transform and dynamic time warping technique [J] . Behdad Ali Journal of Applied Geophysics . 2019,第期

机译：使用连续小波变换和动态时间翘曲技术迈向井日志的实际地层自动相关的一步
4. Satellite image band registration with Dynamic Time Warping and Discrete Wavelet Transform [C] . D. Utku Ufuk, İbrahim S. Açikgöz, Mustafa Teke, Signal Processing and Communications Applications Conference . 2018

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5. Decomposition of spring time series by discrete wavelet transform for characterization of fractured rock aquifers and hydrologic forecasting with artificial neural networks. [D] . Pescatore, John Francis. 2002

机译：利用离散小波变换分解春季时间序列，以表征裂隙含水层，并利用人工神经网络进行水文预报。
6. A Hybrid Forecasting Approach to Air Quality Time Series Based on Endpoint Condition and Combined Forecasting Model [O] . Jiaming Zhu, Peng Wu, Huayou Chen, 2018

机译：基于端点条件和组合预测模型的空气质量时间序列混合预测方法
7. Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system [O] . Ebrahim Eslami, Yunsoo Choi, Yannic Lops, 2020

机译：使用小波变换和动态时间翘曲来识别CNN模型作为空气质量预测系统的局限性

Supplementary material to quot;Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting systemquot;

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