Multi-Time Attention Networks for Irregularly Sampled Time Series (2101.10318v2)

Abstract: Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. In this paper, we propose a new deep learning framework for this setting that we call Multi-Time Attention Networks. Multi-Time Attention Networks learn an embedding of continuous-time values and use an attention mechanism to produce a fixed-length representation of a time series containing a variable number of observations. We investigate the performance of this framework on interpolation and classification tasks using multiple datasets. Our results show that the proposed approach performs as well or better than a range of baseline and recently proposed models while offering significantly faster training times than current state-of-the-art methods.

• The paper introduces Multi-Time Attention Networks (mTANs), a novel methodology utilizing continuous-time embeddings and a flexible time attention mechanism to effectively model sparse, irregularly sampled time series data.
• mTANs learn temporal similarities directly from the data, generating dense temporal representations that demonstrate superior performance on interpolation and classification tasks compared to previous state-of-the-art models.
• Experimental results on real-world datasets show mTANs achieve competitive or better accuracy while significantly reducing training time by up to two orders of magnitude compared to computationally intensive ODE-based methods.

Multi-Time Attention Networks for Irregularly Sampled Time Series

The paper "Multi-Time Attention Networks for Irregularly Sampled Time Series" introduces a novel methodology designed to address the challenges associated with modeling sparse and irregularly sampled time series data, particularly within health-related applications using electronic health records (EHRs). This work is centered around the development of Multi-Time Attention Networks (mTANs), which improve upon existing models by employing a continuous-time embedding mechanism and a time attention mechanism to more effectively capture the nuances presented by irregular sampling.

Background and Motivations

Irregularly sampled time series data are prevalent in many domains, such as healthcare, climate science, and biology, where the data are often sparse and multivariate. Standard machine learning models, including recurrent neural networks (RNNs), typically assume fixed-size, fully observed feature vectors, a presumption that fails with irregularly sampled data. Despite advancements, many existing models inadequately handle temporal irregularity and alignment issues in time series data.

Contributions

The main contributions of the mTAN framework are:

1. Flexible Time Attention Mechanism: Unlike prior approaches that use fixed kernels for temporal similarity, mTANs learn temporal similarity directly from the data using an embedding mechanism and an attention-based model. This results in a more flexible representation.
2. Dense Temporal Representations: The mTAN model captures local structures in time series through a temporally distributed latent representation, thereby providing improved performance on interpolation and classification tasks.
3. Efficiency: By leveraging parallel computation capabilities in its design, the mTANs significantly reduce required training times relative to other recent state-of-the-art models, potentially by one or two orders of magnitude, particularly over models employing neural ordinary differential equations (ODEs).

Methodological Approach

mTANs utilize continuous-time attention embeddings, achieved by embedding time into a vector space, to process irregularly sampled multivariate time series. The multi-time attention module is able to re-represent the time series at fixed reference points using complex attention-based interpolations. The architecture follows an encoder-decoder framework within the variational autoencoder (VAE) framework:

• Encoder: Transforms irregularly sampled input data into a fixed-length latent representation using continuous-time embeddings and RNNs.
• Decoder: Utilizes these latent variables to reconstruct or interpolate the observed data, thereby forming predictions at unobserved time points.

Experimental Validation

The paper validates the proposed approach using three real-world datasets: PhysioNet Challenge 2012, MIMIC-III, and a human activity dataset. Across both interpolation and classification tasks, mTAN consistently shows superior performance in terms of mean squared error (for interpolation) and AUC scores (for classification) when compared to established baselines such as ODE-RNN and Latent ODE models:

1. PhysioNet: mTAN achieved a lower mean squared error for interpolation and a significant improvement in classification AUC scores, outperforming ODE-based methods while reducing computation time substantially.
2. MIMIC-III: Although ODE-RNN models displayed slightly higher AUC scores, mTAN maintained competitive performance with markedly quicker training epochs.
3. Human Activity recognition: mTAND-Enc and mTAND-Full classifiers demonstrated significant improvements over all considered baselines.

Implications and Future Directions

This work presents notable advancements in handling complex time series data using learned time attention mechanisms without the computational overhead of continuous-time model solvers. The flexible mTAN framework can be adapted beyond VAE applications to potentially include GANs or convolutional neural network architectures, which could further optimize computational efficiency.

Future research might explore the extension of mTANs to other domains suffering from similar irregularity issues in time series data or adapt its framework into real-world deployment settings, such as in live monitoring systems within healthcare contexts.

In summary, the introduction of mTANs presents a meaningful contribution to tackling the challenges posed by irregularly sampled time series across various applications, offering both theoretical and practical advancements in computational efficiency and model flexibility.