A Prediction Method with Data Leakage Suppression for Time Series

In view of the characteristics of the collected time series, such as being high noise, non-stationary and nonlinear, most of the current methods are designed to smooth or denoise the whole time series at one time and then divide the training set and testing set, which will lead to using the information of the testing set in the training process, resulting in data leakage and other problems. In order to reduce the impact of noise on time series prediction and prevent data leakage, a prediction method with data leakage suppression for time series (DLS) is proposed. This prediction method carries out multiple variational mode decomposition on the time series by overlapping slicing and improves the noise reduction threshold function to perform noise reduction processing on the decomposed time series. Furthermore, the idea of deep learning is introduced to establish a neural network multi-step prediction model, so as to improve the performance of time series prediction. The different datasets are selected as experimental data, and the results show that the proposed method has a better prediction effect and lower prediction error, compared with the common multi-step prediction methods, which verifies the superiority of the prediction method.