Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning

• URL: http://arxiv.org/abs/2405.16557v1
• Date: Sun, 26 May 2024 13:06:45 GMT
• Title: Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning
• Authors: Chun-Kai Huang, Yi-Hsien Hsieh, Ta-Jung Chien, Li-Cheng Chien, Shao-Hua Sun, Tung-Hung Su, Jia-Horng Kao, Che Lin,
• Abstract summary: We propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. We develop the nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries.
• Score: 6.635084843592727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent imputations, yet these imputed values frequently deviate substantially from their actual counterparts, thereby compromising prediction accuracy. Furthermore, these methods typically fail to provide robust initial embeddings for values infrequently observed or even absent within the training set, posing significant challenges to model generalizability. In response to these challenges, we propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token, effectively bypassing the need for imputation. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. Coupling SCANE with the Transformer Encoder architecture, we develop the Scalable nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries. Our experimental validation, conducted across three disparate electronic health record (EHR) datasets marked by elevated missing value frequencies, confirms the superior performance of SUMMIT over contemporary state-of-the-art approaches addressing similar challenges. These results substantiate the efficacy of SCANE and SUMMIT, underscoring their potential applicability across a broad spectrum of MTS data analytical tasks.

Related papers

• MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation [41.681869408967586]
  Key research question is how to ensure imputation consistency, i.e., intra-consistency between observed and imputed values. Previous methods rely solely on the inductive bias of the imputation targets to guide the learning process.
  arXiv  Detail & Related papers  (2024-08-11T10:24:53Z)
• A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics [51.147876395589925]
  A non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time. A fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance.
  arXiv  Detail & Related papers  (2024-02-26T04:39:01Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme [0.7216399430290167]
  Anomaly detection task for time series, especially for unlabeled data, has been a challenging problem. We address it by applying a suitable data degradation scheme to self-supervised model training. Inspired by the self-attention mechanism, we design a Transformer-based architecture to recognize the temporal context.
  arXiv  Detail & Related papers  (2023-05-08T05:42:24Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data [16.589715330897906]
  MTS suffers from missing data problems, which leads to degradation or collapse of the downstream tasks. This paper presents a universally applicable MTS pre-train model,. -DMAE, to conquer the abovementioned obstacle.
  arXiv  Detail & Related papers  (2022-09-16T08:54:02Z)
• Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data [6.463941665276371]
  We propose a Time-series Representational Learning through Contrastive Predictive Coding (TRL-CPC) towards anomaly detection in MVTS data. First, we jointly optimize an encoder, an auto-regressor and a non-linear transformation function to effectively learn the representations of the MVTS data sets.
  arXiv  Detail & Related papers  (2022-02-08T04:25:29Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values [71.52335136040664]
  We propose a Learning Individual Features (LIFE) framework, which provides a new paradigm for MTS prediction with missing values. LIFE generates reliable features for prediction by using the correlated dimensions as auxiliary information and suppressing the interference from uncorrelated dimensions with missing values. Experiments on three real-world data sets verify the superiority of LIFE to existing state-of-the-art models.
  arXiv  Detail & Related papers  (2021-09-30T04:53:24Z)
• Self-supervised Transformer for Multivariate Clinical Time-Series with Missing Values [7.9405251142099464]
  We present STraTS (Self-supervised Transformer for TimeSeries) model. It treats time-series as a set of observation triplets instead of using the traditional dense matrix representation. It shows better prediction performance than state-of-theart methods for mortality prediction, especially when labeled data is limited.
  arXiv  Detail & Related papers  (2021-07-29T19:39:39Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.