时间序列数据生成近年来越来越受到关注。已经提出了几种生成的对抗网络(GaN)的方法通常是假设目标时间序列数据良好格式化并完成的假设来解决问题。然而,现实世界时间序列(RTS)数据远离该乌托邦,例如,具有可变长度的长序列和信息缺失数据,用于设计强大的发电算法的棘手挑战。在本文中,我们向RTS数据提出了一种新的生成框架 - RTSGAN来解决上述挑战。 RTSGAN首先学习编码器 - 解码器模块,该模块提供时间序列实例和固定维度潜在载体之间的映射,然后学习生成模块以在同一潜在空间中生成vectors。通过组合发电机和解码器,RTSGAN能够生成尊重原始特征分布和时间动态的RTS。为了生成具有缺失值的时间序列,我们进一步用观察嵌入层和决定和生成解码器装备了RTSGAN,以更好地利用信息缺失模式。四个RTS数据集上的实验表明,该框架在用于下游分类和预测任务的合成数据实用程序方面优于前一代方法。
translated by 谷歌翻译
时间序列数据在现实世界应用中无处不在。但是,最常见的问题之一是,时间序列数据可能会通过数据收集过程的固有性质丢失值。因此,必须从多元(相关)时间序列数据中推出缺失值,这对于改善预测性能的同时做出准确的数据驱动决策至关重要。插补的常规工作简单地删除缺失值或基于平均/零填充它们。尽管基于深层神经网络的最新作品显示出了显着的结果,但它们仍然有一个限制来捕获多元时间序列的复杂生成过程。在本文中,我们提出了一种用于多变量时间序列数据的新型插补方法,称为sting(使用GAN基于自我注意的时间序列插补网络)。我们利用生成的对抗网络和双向复发性神经网络来学习时间序列的潜在表示。此外,我们引入了一种新型的注意机制,以捕获整个序列的加权相关性,并避免无关序列带来的潜在偏见。三个现实世界数据集的实验结果表明,刺痛在插补精度以及具有估算值的下游任务方面优于现有的最新方法。
translated by 谷歌翻译
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provides useful insights for better understanding and utilization of missing values in time series analysis.
translated by 谷歌翻译
最近在时间序列域中的合成数据生成的工作集中在使用生成的对抗网络。我们提出了一种用于综合生成时间序列数据的新型架构,使用变分自动编码器(VAES)。拟议的架构具有多种不同的特性:可解释性,编码域知识的能力,以及减少培训时间。我们通过对四个多变量数据集的相似性和可预测性评估数据生成质量。我们试验不同尺寸的培训数据,以测量数据可用性对我们VAE方法的产生质量的影响以及几种最先进的数据生成方法。我们对相似​​性测试的结果表明,VAE方法能够准确地代表原始数据的时间属性。在使用生成数据的下一步预测任务上,所提出的VAE架构一致地满足或超过最先进的数据生成方法的性能。虽然降噪可能导致所生成的数据偏离原始数据,但是我们演示了所产生的去噪数据可以使用生成的数据显着提高下一步预测的性能。最后,所提出的架构可以包含域特定的时间模式,例如多项式趋势和季节性,以提供可解释的输出。这种解释性在需要模型输出的透明度的应用中可以是非常有利的,或者用户希望将时间序列模式的先验知识注入到生成模型中。
translated by 谷歌翻译
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to review the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning.
translated by 谷歌翻译
Generating multivariate time series is a promising approach for sharing sensitive data in many medical, financial, and IoT applications. A common type of multivariate time series originates from a single source such as the biometric measurements from a medical patient. This leads to complex dynamical patterns between individual time series that are hard to learn by typical generation models such as GANs. There is valuable information in those patterns that machine learning models can use to better classify, predict or perform other downstream tasks. We propose a novel framework that takes time series' common origin into account and favors channel/feature relationships preservation. The two key points of our method are: 1) the individual time series are generated from a common point in latent space and 2) a central discriminator favors the preservation of inter-channel/feature dynamics. We demonstrate empirically that our method helps preserve channel/feature correlations and that our synthetic data performs very well in downstream tasks with medical and financial data.
translated by 谷歌翻译
我们提出了一种使用流生理时间序列的端到端模型,以准确预测低氧血症的近期风险,低氧血症是一种罕见但威胁生命的疾病,已知在手术期间造成严重的患者伤害。受到以下事实的启发:低氧血症事件是根据未来观察到的低spo2(即血氧饱和度)实例定义的,我们提出的模型使对未来的低spo2实例和低氧血症结果的混合推断,并由关节序列启用同时优化标签预测的判别解码器的自动编码器,以及对数据重建和预测进行了培训的两个辅助解码器,它们无缝地学习上下文的潜在表示,这些表示捕获了当前状态之间的过渡到未来状态。所有解码器都共享一个基于内存的编码器,有助于捕获患者测量的全局动态。对于一个主要的学术医学中心进行了72,081次手术的大型手术队列,我们​​的模型优于所有基础,包括最先进的低氧预测系统使用的模型。能够以临床上可接受的警报对近期低氧事件的警报进行分辨率的实时预测,尤其是更关键的持续性低氧血症,我们提出的模型在改善临床决策和减轻围手术期的减轻负担方面有希望。
translated by 谷歌翻译
以时间序列形式出现的信号测量是医疗机学习应用中使用的最常见数据类型之一。这样的数据集的大小通常很小,收集和注释昂贵,并且可能涉及隐私问题,这阻碍了我们培训用于生物医学应用的大型,最先进的深度学习模型的能力。对于时间序列数据,我们可以用来扩展数据集大小的数据增强策略套件受到维护信号的基本属性的限制。生成对抗网络(GAN)可以用作另一种数据增强工具。在本文中,我们提出了TTS-CGAN,这是一种基于变压器的条件GAN模型,可以在现有的多级数据集上进行训练,并生成特定于类的合成时间序列序列的任意长度。我们详细介绍了模型架构和设计策略。由我们的模型生成的合成序列与真实的序列无法区分,可以用来补充或替换相同类型的真实信号,从而实现了数据增强的目标。为了评估生成的数据的质量,我们修改小波相干度量指标,以比较两组信号之间的相似性,还可以进行案例研究,其中使用合成和真实数据的混合来训练深度学习模型用于序列分类。与其他可视化技术和定性评估方法一起,我们证明TTS-CGAN生成的合成数据类似于真实数据,并且我们的模型的性能优于为时间序列数据生成而构建的其他最先进的GAN模型。
translated by 谷歌翻译
医疗数据集通常由噪声和缺失数据损坏。这些缺失的模式通常被认为是完全随机的,而是在医学场景中,现实是,这些模式由于在一些时间或数据被收集的不alaled的不均匀方式中被收集的传感器而发生突发。本文建议使用异构数据类型和使用顺序变化自动码器(VAES)来模拟医疗数据记录和突发的缺失数据。特别是,我们提出了一种新的方法,SHI-VAE,其扩展了VAE的能力,使VAE的顺序数据流缺失了观察。我们将我们的模型与精密护理单元数据库(ICU)中的最先进的解决方案进行比较和被动人类监测的数据集。此外,我们发现诸如RMSE的标准错误指标不能得出足够的决定性,以评估时间模型,并包括在我们分析地面真理和算中信号之间的互相关。我们表明Shi-VAE在使用两个指标方面实现了最佳性能,而不是GP-VAE模型的计算复杂性较低,这是用于医疗记录的最先进的方法。
translated by 谷歌翻译
数据质量是发展医疗保健中值得信赖的AI的关键因素。大量具有控制混杂因素的策划数据集可以帮助提高下游AI算法的准确性,鲁棒性和隐私性。但是,访问高质量的数据集受数据获取的技术难度的限制,并且严格的道德限制阻碍了医疗保健数据的大规模共享。数据合成算法生成具有与真实临床数据相似的分布的数据,可以作为解决可信度AI的发展过程中缺乏优质数据的潜在解决方案。然而,最新的数据合成算法,尤其是深度学习算法,更多地集中于成像数据,同时忽略了非成像医疗保健数据的综合,包括临床测量,医疗信号和波形以及电子保健记录(EHRS)(EHRS) 。因此,在本文中,我们将回顾合成算法,尤其是对于非成像医学数据,目的是在该领域提供可信赖的AI。本教程风格的审查论文将对包括算法,评估,局限性和未来研究方向在内的各个方面进行全面描述。
translated by 谷歌翻译
随着深度学习生成模型的最新进展,它在时间序列领域的出色表现并没有花费很长时间。用于与时间序列合作的深度神经网络在很大程度上取决于培训中使用的数据集的广度和一致性。这些类型的特征通常在现实世界中不丰富,在现实世界中,它们通常受到限制,并且通常具有必须保证的隐私限制。因此,一种有效的方法是通过添加噪声或排列并生成新的合成数据来使用\ gls {da}技术增加数据数。它正在系统地审查该领域的当前最新技术,以概述所有可用的算法,并提出对最相关研究的分类法。将评估不同变体的效率;作为过程的重要组成部分,将分析评估性能的不同指标以及有关每个模型的主要问题。这项研究的最终目的是摘要摘要,这些领域的进化和性能会产生更好的结果,以指导该领域的未来研究人员。
translated by 谷歌翻译
Ordinary Differential Equations (ODE)-based models have become popular foundation models to solve many time-series problems. Combining neural ODEs with traditional RNN models has provided the best representation for irregular time series. However, ODE-based models require the trajectory of hidden states to be defined based on the initial observed value or the last available observation. This fact raises questions about how long the generated hidden state is sufficient and whether it is effective when long sequences are used instead of the typically used shorter sequences. In this article, we introduce CrossPyramid, a novel ODE-based model that aims to enhance the generalizability of sequences representation. CrossPyramid does not rely only on the hidden state from the last observed value; it also considers ODE latent representations learned from other samples. The main idea of our proposed model is to define the hidden state for the unobserved values based on the non-linear correlation between samples. Accordingly, CrossPyramid is built with three distinctive parts: (1) ODE Auto-Encoder to learn the best data representation. (2) Pyramidal attention method to categorize the learned representations (hidden state) based on the relationship characteristics between samples. (3) Cross-level ODE-RNN to integrate the previously learned information and provide the final latent state for each sample. Through extensive experiments on partially-observed synthetic and real-world datasets, we show that the proposed architecture can effectively model the long gaps in intermittent series and outperforms state-of-the-art approaches. The results show an average improvement of 10\% on univariate and multivariate datasets for both forecasting and classification tasks.
translated by 谷歌翻译
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
translated by 谷歌翻译
以时间序列形式出现的信号测量是医疗机学习应用中使用的最常见数据类型之一。但是,这样的数据集通常很小,使深度神经网络体系结构的培训无效。对于时间序列,我们可以用来扩展数据集大小的数据增强技巧套件受到维护信号的基本属性的限制。生成对抗网络(GAN)生成的数据可以用作另一个数据增强工具。基于RNN的GAN遭受了这样一个事实,即它们无法有效地模拟具有不规则时间关系的长序列数据点。为了解决这些问题,我们介绍了TTS-GAN,这是一种基于变压器的GAN,可以成功生成与实际长度相似的任意长度的现实合成时间序列数据序列。 GAN模型的生成器和鉴别网络均使用纯变压器编码器体系结构构建。我们使用可视化和降低降低技术来证明真实和生成的时间序列数据的相似性。我们还将生成数据的质量与最佳现有替代方案进行了比较,即基于RNN的时间序列GAN。
translated by 谷歌翻译
现实世界中的电子健康记录(EHR)通常会受到高丢失数据率的困扰。例如,在我们的EHR中,对于某些功能,缺失率可能高达90%,所有功能的平均缺失率约为70%。我们提出了一种时间感知的双交叉访问的缺失价值插补方法,称为ta-dualCV,该方法自发利用跨特征和纵向依赖性的多元依赖性在EHRS中从有限的可观察记录中提取的信息。具体而言,ta-dualCV捕获了不同特征测量值的缺失模式的潜在结构,它还考虑了时间连续性,并根据时间步长和不规则的时间间隔捕获了潜在的时间缺失模式。使用三种类型的任务使用三个大型现实世界EHR评估TA-DUALCV:无监督的选级任务,通过更改掩盖率高达90%的掩码率和使用长期短期记忆(LSTM)进行监督的24小时早期预测对化粪池休克的早期预测(LSTM) 。我们的结果表明,TA-DUALCV在两种任务上的所有现有最先进的归纳基线(例如底特律和驯服)的表现明显好。
translated by 谷歌翻译
Long-range time series forecasting is usually based on one of two existing forecasting strategies: Direct Forecasting and Iterative Forecasting, where the former provides low bias, high variance forecasts and the latter leads to low variance, high bias forecasts. In this paper, we propose a new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data. We theoretically prove that GenF is able to better balance the forecasting variance and bias, leading to a much smaller forecasting error. We implement GenF via three components: (i) a novel conditional Wasserstein Generative Adversarial Network (GAN) based generator for synthetic time series data generation, called CWGAN-TS. (ii) a transformer based predictor, which makes long-range predictions using both generated and observed data. (iii) an information theoretic clustering algorithm to improve the training of both the CWGAN-TS and the transformer based predictor. The experimental results on five public datasets demonstrate that GenF significantly outperforms a diverse range of state-of-the-art benchmarks and classical approaches. Specifically, we find a 5% - 11% improvement in predictive performance (mean absolute error) while having a 15% - 50% reduction in parameters compared to the benchmarks. Lastly, we conduct an ablation study to further explore and demonstrate the effectiveness of the components comprising GenF.
translated by 谷歌翻译
Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks can be used to support decision-making by healthcare professionals. EHRs are structured patient journey data. Each patient journey contains a chronological set of clinical events, and within each clinical event, there is a set of clinical/medical activities. Due to variations of patient conditions and treatment needs, EHR patient journey data has an inherently high degree of missingness that contains important information affecting relationships among variables, including time. Existing deep learning-based models generate imputed values for missing values when learning the relationships. However, imputed data in EHR patient journey data may distort the clinical meaning of the original EHR patient journey data, resulting in classification bias. This paper proposes a novel end-to-end approach to modeling EHR patient journey data with Integrated Convolutional and Recurrent Neural Networks. Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation. Extensive experimental results using the proposed model on two real-world datasets demonstrate robust performance as well as superior prediction accuracy compared to existing state-of-the-art imputation-based prediction methods.
translated by 谷歌翻译
异步时间序列是一个多元时间序列,在该时间序列中,所有通道都被观察到异步独立的,使得时间序列在对齐时极为稀疏。我们经常在具有复杂的观察过程(例如医疗保健,气候科学和天文学)的应用中观察到这种影响,仅举几例。由于异步性质,它们对深度学习体系结构构成了重大挑战,假定给他们的时间序列定期采样,完全观察并与时间对齐。本文提出了一个新颖的框架,我们称深卷积集功能(DCSF),该功能高度可扩展且有效,对于异步时间序列分类任务。随着深度学习体系结构的最新进展,我们引入了一个模型,该模型不变了,在此订单中呈现了时间序列的频道。我们探索卷积神经网络,该网络对定期采样和完全观察到的时间序列的紧密相关的问题分类进行了很好的研究,以编码设置元素。我们评估DCSF的ASTS分类和在线(每个时间点)ASTS分类。我们在多个现实世界和合成数据集上进行的广泛实验验证了建议的模型在准确性和运行时间方面的表现优于一系列最新模型。
translated by 谷歌翻译
隐式神经表示(INRS)最近已成为一种强大的工具,可提供准确和分辨率的数据编码。它们作为一般近似器的稳健性已在各种数据源中显示,并在图像,声音和3D场景表示方面进行了应用。但是,很少有人注意利用这些体系结构来代表和分析时间序列数据。在本文中,我们使用INRS分析了时间序列的表示,从重建精度和训练收敛速度进行比较不同的激活函数。我们展示了如何利用这些网络的时间序列的插补,并在单变量和多变量数据上进行了应用。最后,我们提出了一个超网络架构,该体系结构利用INR来学习整个时间序列数据集的压缩潜在表示。我们引入了基于FFT的损失来指导培训,以便在时间序列中保留所有频率。我们证明该网络可用于将时间序列编码为INR,并且可以将它们的嵌入方式内插以从现有时间序列中生成新的时间序列。我们通过将其用于数据增强来评估我们的生成方法,并表明它与当前的最新方法相对于时间序列的最新方法具有竞争力。
translated by 谷歌翻译
Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences.
translated by 谷歌翻译