Multi-user time series data is critically important in predicting future user behavior across various fields, including communication and finance. However, due to data skewness, user heterogeneity, and time shift, it becomes challenging to predict multiple types of user time series effectively. Currently, there are few efficient time series prediction methods that address these issues. In this paper, we propose a dynamic time warping based radial basis function neural network model for multi-user time series prediction, named DTW-RBFNN. To address data skewness, it introduces an effective preprocessing function of logarithmic power function to adjust the users' range and distribution. K-means based on DTW and DBA was utilized to eliminate the time-shift issue and obtain proper centers for the RBFNN model. Finally, the DTW-RBFNN model overcomes user heterogeneity and directly predicts the time series of different users in a unified manner. Our experimental evaluation on real-world user communication traffic data and bank user deposit data demonstrates the superiority of the proposed DTW-RBFNN model over the traditional fixed effects model and deep neural network model LSTM.