Multivariate time series data is ubiquitous in the real world, and the study of its modeling and analysis is a popular research topic in meteorology, transportation, finance and other fields. In these studies, classical statistical methods are primarily aimed at single time series analysis, while deep learning demonstrates the power to mine patterns from massive amounts of data. A major application of these studies is to analyze collected historical sequence information to predict what will happen over time in the future. Currently, recurrent neural network-based models and time-convolution-based models realize the predictive power of multivariate time series, but these deep models perform mediocrely at predicting long-sequence tasks. On the one hand, due to the accumulation of errors, on the other hand, the fact that the collected sequence contains a large amount of high-frequency noise. In order to improve the prediction accuracy of the model and mine more valuable features from the series, we propose a novel multivariate time series prediction framework ADWT for time series modeling. By designing an adaptive filtering module in the characteristics of the signal frequency domain, our model removes noise from some of the time series and builds an end-to-end framework by fusing it with the prediction module of deep learning. Experimental results show that our model can effectively improve the prediction accuracy of multivariate time series, and its performance in the three benchmark data sets is competitive with the latest spatial-temporal series prediction model, and has good interpretability.