[Paper Review] TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series

Yang Jiao, Kai Yang+4 more|arXiv (Cornell University)|Oct 4, 2020

Time Series Analysis and Forecasting47 references4 citations

TL;DR

TimeAutoML proposes an autonomous, self-supervised representation learning framework for multivariate irregularly sampled time series, leveraging automatic hyperparameter optimization, contrastive learning with negative sample generation, and an auxiliary classification task. It achieves state-of-the-art performance, outperforming all baselines on 78 out of 85 UCR datasets in anomaly detection with up to 20% AUC improvement.

ABSTRACT

Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks. To obtain an effective representation of MTS data, it is not only essential to consider unpredictable dynamics and highly variable lengths of these data but also important to address the irregularities in the sampling rates of MTS. Existing parametric approaches rely on manual hyperparameter tuning and may cost a huge amount of labor effort. Therefore, it is desirable to learn the representation automatically and efficiently. To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths. As opposed to previous works, we first present a representation learning pipeline in which the configuration and hyperparameter optimization are fully automatic and can be tailored for various tasks, e.g., anomaly detection, clustering, etc. Next, a negative sample generation approach and an auxiliary classification task are developed and integrated within TimeAutoML to enhance its representation capability. Extensive empirical studies on real-world datasets demonstrate that the proposed TimeAutoML outperforms competing approaches on various tasks by a large margin. In fact, it achieves the best anomaly detection performance among all comparison algorithms on 78 out of all 85 UCR datasets, acquiring up to 20% performance improvement in terms of AUC score.

Motivation & Objective

• To address the challenge of learning effective representations for multivariate time series with irregular sampling rates and variable lengths.
• To eliminate manual hyperparameter tuning by automating the entire representation learning pipeline configuration.
• To enhance representation quality through a novel negative sample generation method and an auxiliary classification task.
• To enable robust performance across diverse downstream tasks such as anomaly detection and clustering on real-world irregular time series data.

Proposed method

• TimeAutoML employs an end-to-end autoencoder-based architecture with a learnable positional encoding to handle irregular sampling intervals.
• It integrates a contrastive learning objective using a negative sample generation strategy to improve representation discrimination.
• An auxiliary classification head is introduced to distinguish normal time series from generated negative samples, enhancing feature learning.
• The framework uses a neural architecture search (NAS) strategy to automatically select optimal modules and hyperparameters for each task.
• Latent space representations are used for downstream tasks like clustering (via GMM) and anomaly detection (via density estimation).
• A self-supervised loss function, $ L_{\mathrm{self}} $, is designed to generate pseudo-labels from unlabeled data, enabling end-to-end training without supervision.

Experimental results

Research questions

• RQ1 Can an autonomous representation learning framework outperform manual-tuned baselines on irregularly sampled multivariate time series?
• RQ2 How does TimeAutoML perform across diverse downstream tasks such as anomaly detection and clustering under varying irregular sampling rates?
• RQ3 What is the contribution of the negative sample generation and auxiliary classification task to the model's representation capability?
• RQ4 How robust is TimeAutoML to data contamination and varying sampling irregularities?

Key findings

• TimeAutoML achieves the best anomaly detection performance on 78 out of 85 UCR datasets, with up to a 20% improvement in AUC score compared to state-of-the-art methods.
• The model maintains strong robustness across irregular sampling rates, with AUC scores stable even when the sampling rate irregularity $ \beta $ increases from 0 to 0.7.
• On multivariate time series clustering tasks, TimeAutoML achieves the highest NMI scores on all five evaluated datasets, outperforming methods like DTCR, K-shape, and SPIRAL.
• Ablation studies show that removing the self-supervised loss $ L_{\mathrm{self}} $ leads to a performance drop, confirming its importance in representation learning.
• Visualization in the latent space confirms that normal time series cluster densely, while anomalous sequences are clearly separated, enabling interpretable anomaly detection.
• The framework demonstrates strong generalization, with performance degradation only slightly increasing under data contamination, indicating resilience to noisy inputs.