Information diffusion prediction is an important task which studies how information items spread among users. Generally, information diffusion prediction is divided into microscopic, macroscopic, and unified multi-scale information prediction, which aims to predict the next influenced user, the total number of influenced users, or the above two within a unified framework, respectively. However, the existing prediction models just adopted the diffusion sequences information of cascades, ignoring two important information for diffusion prediction, i.e. the specific diffusion timestamp and the wide dispersion. The former contains the dynamic changes of users' repost preferences and the diffusion rate, while the latter reflects the user's aggregation of influenced along diffusion paths. In this paper, we propose a novel Multi-Scale Information Diffusion Prediction model (MSIDP) for predicting microscopic and macroscopic information diffusion simultaneously. MSIDP encodes timestamp information and designs a bidirectional graph convolutional network (BGCN) to jointly learn the representations of users from the deep propagation and the wide dispersion, such that more diffusion information can be captured. Extensive experiments on three real datasets demonstrate the effectiveness of our proposed model compared with 11 baseline algorithms in terms of two evaluation metrics.