2021/02/16 by Dongjin Lee, Kijung Shin, Lee, Dongjin +1
Computer Science · Engineering · Mathematics · #Databases (cs.DB) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.2102.08466
openalex publication_date 2021/02/16 · openalex created_date 2021/03/01 · openalex updated_date 2026/07/28
Consider multiple seasonal time series being collected in real-time, in the form of a tensor stream. Real-world tensor streams often include missing entries (e.g., due to network disconnection) and at the same time unexpected outliers (e.g., due to system errors). Given such a real-world tensor stream, how can we estimate missing entries and predict future evolution accurately in real-time? In this work, we answer this question by introducing SOFIA, a robust factorization method for real-world tensor streams. In a nutshell, SOFIA smoothly and tightly integrates tensor factorization, outlier removal, and temporal-pattern detection, which naturally reinforce each other. Moreover, SOFIA integrates them in linear time, in an online manner, despite the presence of missing entries. We experimentally show that SOFIA is (a) robust and accurate: yielding up to 76% lower imputation error and 71% lower forecasting error; (b) fast: up to 935X faster than the second-most accurate competitor; and (c) scalable: scaling linearly with the number of new entries per time step.