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

Sun, C. P.; Hong, Shenda; Song, Moxian; Zhou, Yihang; Sun, Yongyue; Cai, Derun; Li, Hongyan

doi:10.48550/arxiv.2105.00412

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regretfully, these models are capable of inferring missing data and performing regression/classification tasks simultaneously but cannot make prediction. A model named Time Encoding-Encoding Echo State Network (TE-ESN) is designed to support early prediction and one-step-ahead forecasting on irregularly sampled time series [26]. However, these models cannot predict multistep data values of any desired time.…”

Section: One-stage Methodsmentioning

confidence: 99%

Time-aware neural ordinary differential equations for incomplete time series modeling

et al. 2023

View full text Add to dashboard Cite

Internet of Things realizes the ubiquitous connection of all things, generating countless time-tagged data called time series. However, real-world time series are often plagued with missing values on account of noise or malfunctioning sensors. Existing methods for modeling such incomplete time series typically involve preprocessing steps, such as deletion or missing data imputation using statistical learning or machine learning methods. Unfortunately, these methods unavoidable destroy time information and bring error accumulation to the subsequent model. To this end, this paper introduces a novel continuous neural network architecture, named Time-aware Neural-Ordinary Differential Equations (TN-ODE), for incomplete time data modeling. The proposed method not only supports imputation missing values at arbitrary time points, but also enables multi-step prediction at desired time points. Specifically, TN-ODE employs a time-aware Long Short-Term Memory as an encoder, which effectively learns the posterior distribution from partial observed data. Additionally, the derivative of latent states is parameterized with a fully connected network, thereby enabling continuous-time latent dynamics generation. The proposed TN-ODE model is evaluated on both real-world and synthetic incomplete time-series datasets by conducting data interpolation and extrapolation tasks as well as classification task. Extensive experiments show the TN-ODE model outperforms baseline methods in terms of Mean Square Error for imputation and prediction tasks, as well as accuracy in downstream classification task.

show abstract

Section: One-stage Methodsmentioning

confidence: 99%

Time-aware neural ordinary differential equations for incomplete time series modeling

et al. 2023

View full text Add to dashboard Cite

show abstract

“…With the advancement of medical technology, patients in the Intensive Care Unit (ICU) are monitored by different instruments at the bedside that measure different vital signals Sun et al (2021) about the patient's health. Such as heart rate, systolic blood pressure, temperature, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction

Niu

Zhang²,

Peng³

et al. 2023

Front. Mol. Biosci.

View full text Add to dashboard Cite

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients’ time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient’s clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient’s time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.

show abstract

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

Cited by 2 publications

References 15 publications

Time-aware neural ordinary differential equations for incomplete time series modeling

Time-aware neural ordinary differential equations for incomplete time series modeling

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction

Contact Info

Product

Resources

About