Multivariate Time-series Imputation with Disentangled Temporal Representations

Multivariate time series often faces the problem of missing value. Many time series imputation methods have been developed in literature. However, they all rely on an entangled representation to model dynamics of time series, which may fail to fully exploit the multiple factors (e.g., periodic patterns) presented in the data. Moreover, the entangled representations usually have no semantic meaning, and thus they often lack interpretability. In addition, many recent models are proposed to deal with the whole time series to identify temporal dynamics, but they are not scalable to long time series. Different from existing approaches, we propose TIDER, a novel matrix factorization-based method with disentangled temporal representations that account for multiple factors, namely trend, seasonality, and local bias, to model complex dynamics. The learned disentanglement makes the imputation process more reliable and offers explainability for imputation results. Moreover, TIDER is scalable to long time series. Empirical results show that our method outperforms existing approaches on three typical real-world datasets, especially on long time series, reducing mean absolute error by up to 50%. It also scales well to long datasets on which existing deep learning based methods struggle. Disentanglement validation experiments further highlight the robustness and accuracy of our model. * The main part of Xiucheng's work is done when he is in Nanyang Technological University.