HTF: An Effective Algorithm for Time Series to Recover Missing Blocks

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.