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Learning Long-Term Dependencies in Irregularly-Sampled Time Series [article]

Mathias Lechner, Ramin Hasani
2020 arXiv   pre-print
Recurrent neural networks (RNNs) with continuous-time hidden states are a natural fit for modeling irregularly-sampled time series.  ...  We experimentally show that ODE-LSTMs outperform advanced RNN-based counterparts on non-uniformly sampled data with long-term dependencies.  ...  As a solution, we propose ODE-LSTMs, a continuous-time RNN model capable of learning long-term dependencies of irregularly-sampled time-series.  ... 
arXiv:2006.04418v4 fatcat:c3nx2sxchbeanbptqsib4b2oye

Clinical time series prediction: Toward a hierarchical dynamical system framework

Zitao Liu, Milos Hauskrecht
2015 Artificial Intelligence in Medicine  
In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations.  ...  Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive  ...  These contains both short-term and long-term predictions depending on the difference in between the time at which we predict the value and the time of the last observation seen.  ... 
doi:10.1016/j.artmed.2014.10.005 pmid:25534671 pmcid:PMC4422790 fatcat:2c3mq5hajfac5m3f4o6iq633yu

TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data [article]

Chenxi Sun and Shenda Hong and Moxian Song and Yanxiu Zhou and Yongyue Sun and Derun Cai and Hongyan Li
2021 arXiv   pre-print
However, existing methods have suboptimal predictions due to artificially introducing new dependencies in a time series and biasedly learning relations among time series when modeling these two characteristics  ...  Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern in the real-world applications. For more accurate prediction, the methods had better grasp more data characteristics.  ...  LS has the greatest impact in USHCN and SILSO, as there are many long time series, it is necessary to learn the dependence in different time spans.  ... 
arXiv:2105.00412v1 fatcat:qngcbfjg5jbwtggvppovrk2gqa

Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

Zitao Liu, Milos Hauskrecht
2016 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
time series prediction models in terms of prediction accuracy.  ...  To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths.  ...  Acknowledgment The work in this paper was supported by grant R01GM088224 from the NIH.  ... 
doi:10.1609/aaai.v30i1.10181 fatcat:asj2k2he55gxvpwvstwqyyxwsm

Interpolation-Prediction Networks for Irregularly Sampled Time Series [article]

Satya Narayan Shukla, Benjamin M. Marlin
2019 arXiv   pre-print
In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series.  ...  This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate.  ...  an irregularly sampled time series as input.  ... 
arXiv:1909.07782v1 fatcat:rsxt72oqpbc2tfagei35ftpznu

Multi-view Integration Learning for Irregularly-sampled Clinical Time Series [article]

Yurim Lee, Eunji Jun, Heung-Il Suk
2021 arXiv   pre-print
In this work, we propose a multi-view features integration learning from irregular multivariate time series data by self-attention mechanism in an imputation-free manner.  ...  Specifically, we devise a novel multi-integration attention module (MIAM) to extract complex information inherent in irregular time series data.  ...  the long-term memories.  ... 
arXiv:2101.09986v2 fatcat:u46qtifgbjer3odmjtmu3ynxfq

Forecasting in multivariate irregularly sampled time series with missing values [article]

Shivam Srivastava, Prithviraj Sen, Berthold Reinwald
2020 arXiv   pre-print
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains.  ...  In forecasting, it is necessary to not only forecast the right value but also to forecast when that value will occur in the irregular time series.  ...  Importantly, the ability of the GRU to learn long-term dependencies is a significant advantage.  ... 
arXiv:2004.03398v1 fatcat:6txnnflstjf6loo4vas6hbj6fq

Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

Zitao Liu, Milos Hauskrecht
2016 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
time series prediction models in terms of prediction accuracy.  ...  To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths.  ...  Acknowledgments The work presented in this paper was supported by grant R01GM088224 from the NIH.  ... 
pmid:27525189 pmcid:PMC4980099 fatcat:euu3a267wzhzbnr5kf75bijsqq

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data [article]

Chenxi Sun, Shenda Hong, Moxian Song, Hongyan Li
2020 arXiv   pre-print
Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences.  ...  Especially in the medical environment, the widely used Electronic Health Records (EHRs) have abundant typical irregularly sampled medical time series (ISMTS) data.  ...  Table 1 : 1 Abbreviations Full name Abbreviations Full name Abbreviations Time series TS Recurrent neural network RNN Irregularly sampled time series ISTS Long short-term unit LSTM Irregularly  ... 
arXiv:2010.12493v2 fatcat:hyx3tab4svgptewwp6lhz2q6wy

Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time [article]

Rafael T. Sousa, Lucas A. Pereira, Anderson S. Soares
2020 arXiv   pre-print
Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals.  ...  The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records.  ...  Most machine learning methods do not have time comprehension, this means they only consider observation order. This makes it harder to learn time dependencies found in time series problems.  ... 
arXiv:2003.09291v1 fatcat:e4f6nc5qwnfjnndrjlsbuhgqhy

Graph-Guided Network for Irregularly Sampled Multivariate Time Series [article]

Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik
2022 arXiv   pre-print
In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors  ...  Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data.  ...  M.Z. is supported, in part, by NSF under nos.  ... 
arXiv:2110.05357v2 fatcat:6tdi5aon3nd5larqkw5hy3qb3y

Set Functions for Time Series [article]

Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt
2020 arXiv   pre-print
datasets of long time series and online monitoring scenarios.  ...  Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially  ...  that cover long-term trends, transients, and also sampling information.  ... 
arXiv:1909.12064v3 fatcat:2sjbyvc37rglndluspjolt2g5a

Fractional SDE-Net: Generation of Time Series Data with Long-term Memory [article]

Kohei Hayashi, Kei Nakagawa
2022 arXiv   pre-print
Time series data, especially from hydrology, telecommunications, economics, and finance, exhibit long-term memory also called long-range dependency (LRD).  ...  It is often the case that input time-series data have only one realized (and usually irregularly sampled) path, which makes it difficult to extract time-series characteristics, and its noise structure  ...  In summary, we revealed that the fSDE-Net we introduced in this paper is a generative model which is applicable for irregularly-sampled time series and inherit the long-range dependency.  ... 
arXiv:2201.05974v2 fatcat:wcwodch2xnfnpadv3ttgzf3ipy

NRTSI: Non-Recurrent Time Series Imputation [article]

Siyuan Shan, Yang Li, Junier B. Oliva
2021 arXiv   pre-print
In addition, NRTSI can directly handle irregularly-sampled time series, perform multiple-mode stochastic imputation, and handle data with partially observed dimensions.  ...  Time series imputation is a fundamental task for understanding time series with missing data.  ...  Irregularly-sampled Time Series RNN is the dominant model for high-dimensional, regularlysampled time series. However, it is not suitable for irregularly-sampled time series.  ... 
arXiv:2102.03340v3 fatcat:itv6z2x4zrf55gxxvt2py5yrlm

Clinical Time Series Prediction with a Hierarchical Dynamical System [chapter]

Zitao Liu, Milos Hauskrecht
2013 Lecture Notes in Computer Science  
We test our framework on the problem of learning clinical time series data from the complete blood count panel, and show that our framework outperforms alternative time series models in terms of its predictive  ...  In this work we develop and test a novel hierarchical framework for modeling and learning multivariate clinical time series data.  ...  However, observations in clinical time series are often spaced irregularly in time.  ... 
doi:10.1007/978-3-642-38326-7_34 fatcat:2uatyy5ptnbgtjsutpw32adddq
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