A Consumer Behavior Prediction Model Based on Multivariate Real-Time Sequence Analysis

With the rapid development of online finance and social networks, a large amount behavioral data is stored on Internet, which can fully reflect shopping tendencies habits real users. Using big to analyze consumer behavior more scientific accurate than traditional sampling survey method. Internet consumption are time series data. Therefore, this paper proposes method analyzing sequence data, learns personal interests habits, finally predicts payment behavior. The experiments compare execution effect different algorithms multiple databases verify feasibility effectiveness proposed algorithm SeqLearn.