scholarly journals A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series

2019 ◽  
Author(s):  
Yale Chang ◽  
Jonathan Rubin ◽  
Gregory Boverman ◽  
Shruti Vij ◽  
Asif Rahman ◽  
...  
Keyword(s):  
Time Series ◽  
Early Prediction ◽  
Neural Architecture

scholarly journals 2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2392
Author(s):  
Antonello Rosato ◽  
Rodolfo Araneo ◽  
Amedeo Andreotti ◽  
Federico Succetti ◽  
Massimo Panella
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Neural Architecture ◽  
Network Layers ◽  
Learning Scheme ◽  
World Energy ◽  
Multivariate Prediction ◽  
Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

scholarly journals Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach

2021 ◽  
Vol 5 (1) ◽  
pp. 12
Author(s):  
Christos Bazinas ◽  
Eleni Vrochidou ◽  
Chris Lytridis ◽  
Vassilis Kaburlasos
Keyword(s):  
Time Series ◽  
Accurate Prediction ◽  
Computational Experiments ◽  
Stationary Time Series ◽  
Neural Architecture ◽  
Recursive Scheme ◽  
Stationary Time ◽  
Agricultural Applications ◽  
Data Statistics

This work represents any distribution of data by an Intervals’ Number (IN), hence it represents all-order data statistics, using a “small” number of L intervals. The INs considered are induced from images of grapes that ripen. The objective is the accurate prediction of grape maturity. Based on an established algebra of INs, an optimizable IN-regressor is proposed, implementable on a neural architecture, toward predicting future INs from past INs. A recursive scheme tests the capacity of the IN-regressor to learn the physical “law” that generates the non-stationary time-series of INs. Computational experiments demonstrate comparatively the effectiveness of the proposed techniques.

scholarly journals Multiscale network representation of physiological time series for early prediction of sepsis

2017 ◽  
Vol 38 (12) ◽  
pp. 2235-2248 ◽  
Author(s):  
Supreeth P Shashikumar ◽  
Qiao Li ◽  
Gari D Clifford ◽  
Shamim Nemati
Keyword(s):  
Time Series ◽  
Early Prediction ◽  
Network Representation ◽  
Physiological Time

scholarly journals Classification of cardiovascular time series based on different coupling structures using recurrence networks analysis

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110623 ◽  
Author(s):  
Gonzalo Marcelo Ramírez Ávila ◽  
Andrej Gapelyuk ◽  
Norbert Marwan ◽  
Thomas Walther ◽  
Holger Stepan ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Time Series ◽  
Path Length ◽  
Clustering Coefficient ◽  
Early Prediction ◽  
Quadratic Discriminant Analysis ◽  
Novel Approach ◽  
Networks Analysis

We analyse cardiovascular time series with the aim of performing early prediction of preeclampsia (PE), a pregnancy-specific disorder causing maternal and foetal morbidity and mortality. The analysis is made using a novel approach, namely the ε -recurrence networks applied to a phase space constructed by means of the time series of the variabilities of the heart rate and the blood pressure (systolic and diastolic). All the possible coupling structures among these variables are considered for the analysis. Network measures such as average path length, mean coreness, global clustering coefficient and scale-local transitivity dimension are computed and constitute the parameters for the subsequent quadratic discriminant analysis. This allows us to predict PE with a sensitivity of 91.7 per cent and a specificity of 68.1 per cent, thus validating the use of this method for classifying healthy and preeclamptic patients.

scholarly journals Using Features Extracted from Vital Time Series for Early Prediction of Sepsis

2019 ◽  
Author(s):  
Qiang Yu ◽  
Xiaolin Huang ◽  
Weifeng Li ◽  
Cheng Wang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Early Prediction
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