Time Series Imputation

TL;DR

This paper introduces a novel imputation method for multivariate time series with missing data, leveraging Expectation Maximization over dynamic Bayesian networks, which outperforms existing methods especially with categorical variables.

Contribution

The paper proposes a new imputation technique based on Expectation Maximization and dynamic Bayesian networks, addressing limitations of regression-based methods for categorical data.

Findings

Outperforms several state-of-the-art imputation methods

Effective on both synthetic and real datasets

Handles categorical variables better than existing approaches

Abstract

Multivariate time series is a very active topic in the research community and many machine learning tasks are being used in order to extract information from this type of data. However, in real-world problems data has missing values, which may difficult the application of machine learning techniques to extract information. In this paper we focus on the task of imputation of time series. Many imputation methods for time series are based on regression methods. Unfortunately, these methods perform poorly when the variables are categorical. To address this case, we propose a new imputation method based on Expectation Maximization over dynamic Bayesian networks. The approach is assessed with synthetic and real data, and it outperforms several state-of-the art methods.