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HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Nov2021, Vol. 33 Issue 11, p3582-3593. 12p. - Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 33
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 153711844
- Full Text :
- https://doi.org/10.1109/TKDE.2020.2971190