RM2T2C: Retrospective Multivariate Multistep Transition Tensor Chain Model for User Mobility Pattern Prediction

Abstract

With the bloom of intelligent devices over the world, a large scale of user’s trajectory data are collected. How to mine valuable rules from these data and provide services for the industrial community has become an urgent problem. In this article, we propose a multimodal prediction system to infer users’ mobility pattern embedded in heterogeneous data from cyber–physical–social space. According to users’ mobility pattern, the framework can provide smart services for the industrial community. The highlight is the retrospective multivariate multistep transition tensor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{M}^2}{\text{T}^2}$</tex-math></inline-formula> ) chain model, which decomposes a large scale of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{M}^2}{\text{T}^2}$</tex-math></inline-formula> into a series of small-scale transition tensors (subtransition tensors) with the tensor maximum likelihood estimation method. Then, that one can solve the stationary probability distribution with the small-scale subtransition tensors so as to highly reduce the computation and storage cost. At the same time, the tensor maximum likelihood estimation method avoids the overfitting of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{M}^2}{\text{T}^2}$</tex-math></inline-formula> , so the proposed model improves the performance of prediction systems. In the end, several experiments are constructed to evaluate the proposed model.