Generative Semi-supervised Learning for Multivariate Time Series Imputation
The missing values, widely existed in multivariate time series data, hinder the effective data analysis. Existing time series imputation methods do not make full use of the label information in real-life time series data. In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data. It consists of three players, i.e., a generator, a discriminator, and a classifier. The classifier predicts labels of time series data, and thus it drives the generator to estimate the missing values (or components), conditioned on observed components and data labels at the same time. We introduce a temporal reminder matrix to help the discriminator better distinguish the observed components from the imputed ones. Moreover, we theoretically prove that, SSGAN using the temporal reminder matrix and the classifier does learn to estimate missing values converging to the true data distribution when the Nash equilibrium is achieved. Extensive experiments on three public real-world datasets demonstrate that, SSGAN yields a more than 15% gain in performance, compared with the state-of-the-art methods.
Code (1)
Tasks
Generative Adversarial NetworkImputationMissing ValuesMultivariate Time Series ImputationTime SeriesTime Series ClassificationSimilar Papers 제목 키워드 기반
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series
Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years. In particular, label shortage is a real challenge for the classification task on MTS, considering its complex dimensional a…
Representation LearningTime SeriesTime Series AnalysisModeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representat…
Time SeriesUncertainty QuantificationSelf-Supervised Learning of Disentangled Representations for Multivariate Time-Series
Multivariate time-series data in fields like healthcare and industry are informative but challenging due to high dimensionality and lack of labels. Recent self-supervised learning methods excel in learning rich represent…
Inductive BiasRepresentation LearningSelf-Supervised LearningTime SeriesrETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data
Deep neural networks for time series must capture complex temporal patterns, to effectively represent dynamic data. Self- and semi-supervised learning methods show promising results in pre-training large models, which --…
Time Series ClassificationNeural Contextual Anomaly Detection for Time Series
We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multi…
Anomaly DetectionContextual Anomaly DetectionRepresentation LearningTime Series+1