To accurately analyze the dynamics and changing trends of KPI (Key Performance Indicator) data in the daily monitoring of cloud computing clusters and predict its subsequent development to achieve high availability of cloud computing clusters, we propose a three-frequency cloud KPI data prediction approach based on combined attention model of EWT-ARIMA-Auto-TPA (EAAT for short).First, low, medium and high frequency Intrinsic Mode Variables (IMFs) of cloud KPI data are obtained via Empirical Wavelet Transform (EWT) to reduce the complexity of data prediction.Second, according to the information characteristics of low, medium and high frequency IMFs obtained from the decomposition, models of ARIMA, Autoformer, and TPA-BiLSTM are used to predict each type of IMFs.Finally, the classification prediction results are combined through the Inverse EWT (IEWT) to obtain the prediction result of the KPI.The proposed prediction approach has been verified on four datasets from Google and Amazon.Whether the data is periodic and stable or not, the proposed approach outperforms comparison models.