Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism
This work addresses a critical issue in time series imputation for fields like healthcare and IoT by handling missing mechanism mismatches, though it appears incremental as it builds on existing variational inference and normalizing flow methods.
The paper tackles the problem of time series imputation by addressing different missing mechanisms (MAR and MNAR), proposing a framework that tailors solutions to each mechanism and establishes identifiability results, with experiments showing it surpasses existing techniques across various datasets.
Time series imputation is one of the most challenge problems and has broad applications in various fields like health care and the Internet of Things. Existing methods mainly aim to model the temporally latent dependencies and the generation process from the observed time series data. In real-world scenarios, different types of missing mechanisms, like MAR (Missing At Random), and MNAR (Missing Not At Random) can occur in time series data. However, existing methods often overlook the difference among the aforementioned missing mechanisms and use a single model for time series imputation, which can easily lead to misleading results due to mechanism mismatching. In this paper, we propose a framework for time series imputation problem by exploring Different Missing Mechanisms (DMM in short) and tailoring solutions accordingly. Specifically, we first analyze the data generation processes with temporal latent states and missing cause variables for different mechanisms. Sequentially, we model these generation processes via variational inference and estimate prior distributions of latent variables via normalizing flow-based neural architecture. Furthermore, we establish identifiability results under the nonlinear independent component analysis framework to show that latent variables are identifiable. Experimental results show that our method surpasses existing time series imputation techniques across various datasets with different missing mechanisms, demonstrating its effectiveness in real-world applications.