Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism

TL;DR

This paper introduces a novel framework for time series imputation that explicitly considers different missing data mechanisms, using variational inference and normalizing flows to improve accuracy and identifiability.

Contribution

It proposes a mechanism-aware imputation framework with theoretical identifiability results, addressing limitations of existing methods that ignore missing mechanism differences.

Findings

Outperforms existing imputation methods across various datasets.

Provides theoretical guarantees of latent variable identifiability.

Effectively models different missing mechanisms in real-world scenarios.

Abstract

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.