HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization

Recovering missing values in time series is critical when performing time series analysis. And the blackouts issue studied in this paper, described as losing all the data during a certain period, is among the most urgent issues due to its devastating impact on service quality, and is challenging because of the absence of coevolving data sequences for reference. As a result, many existing approaches that rely on data from other coevolving sequences for missing value recovery are infeasible in handling blackouts. To address the issue, this work proposes a novel Hankel matrix factorization approach, HKMF-T, to recover missing values during blackouts for tagged time series, where a tagged time series consists of a data sequence and a corresponding tag sequence. Motivated by real-world observations, HKMF-T decomposes the data sequence into two components: 1) an internal, slowly-varying smooth trend, and 2) external impacts indicated by the tag sequence. By transforming a partially observed data sequence into a corresponding Hankel matrix, we learn the above two components and estimate the missing values under a unified framework of Hankel matrix factorization. Extensive experiments are conducted to evaluate the practical performance of HKMF-T on real-world data sets. And the results suggest HKMF-T outperforms the baseline approaches for blackouts with long durations.