TY - GEN
T1 - HTF
T2 - International Conference on Database Systems for Advanced Applications, DASFAA 2021 held in conjunction with BDQM 2021, GDMA 2021, MLDLDSA 2021, MobiSocial 2021 and MUST 2021
AU - Zhang, Haijun
AU - Gao, Hong
AU - Jin, Dailiang
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - With the popularity of time series analysis, failure during data recording, transmission, and storage makes missing blocks in time series a problem to be solved. Therefore, it is of great significance to study effective methods to recover missing blocks in time series for better analysis and mining. In this paper, we focus on the situation of continuous missing blocks in multivariate time series. Aiming at the blackout missing block pattern, we propose a method called hankelized tensor factorization (HTF), based on singular spectrum analysis (SSA). After the hankelization of the time series, this method decomposes the intermediate result into the product of time-evolving embedding, time delaying embedding, and hidden variables embedding of multivariate variables in the low-dimensional space, to learn the essence of time series. In an experimental benchmark containing 5 data sets, the recovery effect of HTF and other baseline methods in three missing block patterns are compared to evaluate the performance of HTF. Results show that when the missing block pattern is blackout, the HTF method achieves the best recovery effect, and it can also have good results for other missing patterns.
AB - With the popularity of time series analysis, failure during data recording, transmission, and storage makes missing blocks in time series a problem to be solved. Therefore, it is of great significance to study effective methods to recover missing blocks in time series for better analysis and mining. In this paper, we focus on the situation of continuous missing blocks in multivariate time series. Aiming at the blackout missing block pattern, we propose a method called hankelized tensor factorization (HTF), based on singular spectrum analysis (SSA). After the hankelization of the time series, this method decomposes the intermediate result into the product of time-evolving embedding, time delaying embedding, and hidden variables embedding of multivariate variables in the low-dimensional space, to learn the essence of time series. In an experimental benchmark containing 5 data sets, the recovery effect of HTF and other baseline methods in three missing block patterns are compared to evaluate the performance of HTF. Results show that when the missing block pattern is blackout, the HTF method achieves the best recovery effect, and it can also have good results for other missing patterns.
KW - Missing block pattern
KW - Missing value recovery
KW - Multivariate time series
KW - Tensor factorization
UR - https://www.scopus.com/pages/publications/85104710905
U2 - 10.1007/978-3-030-73216-5_3
DO - 10.1007/978-3-030-73216-5_3
M3 - 会议稿件
AN - SCOPUS:85104710905
SN - 9783030732158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 44
BT - Database Systems for Advanced Applications
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Lee, Wang-Chien
A2 - Tseng, Vincent S.
A2 - Kalogeraki, Vana
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 April 2021 through 14 April 2021
ER -