[HTML][HTML] Deep imputation of missing values in time series health data: A review with benchmarking
M Kazijevs, MD Samad - Journal of biomedical informatics, 2023 - Elsevier
The imputation of missing values in multivariate time series (MTS) data is a critical step in
ensuring data quality and producing reliable data-driven predictive models. Apart from many …
ensuring data quality and producing reliable data-driven predictive models. Apart from many …
DAFA-BiLSTM: Deep autoregression feature augmented bidirectional LSTM network for time series prediction
H Wang, Y Zhang, J Liang, L Liu - Neural Networks, 2023 - Elsevier
Time series forecasting models that use the past information of exogenous or endogenous
sequences to forecast future series play an important role in the real world because most …
sequences to forecast future series play an important role in the real world because most …
Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption
Accurate energy consumption prediction models can bring tremendous benefits to building
energy efficiency, where the use of data-driven models allows models to be trained based …
energy efficiency, where the use of data-driven models allows models to be trained based …
A customized deep learning approach to integrate network-scale online traffic data imputation and prediction
Online data imputation and traffic prediction based on real-time data streams are essential
for the intelligent transportation systems, particularly online navigation applications based …
for the intelligent transportation systems, particularly online navigation applications based …
[HTML][HTML] Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application
values in the maritime industry. However, using such big data from the Automatic …
values in the maritime industry. However, using such big data from the Automatic …
[HTML][HTML] LSTM recurrent neural network for hand gesture recognition using EMG signals
A Toro-Ossaba, J Jaramillo-Tigreros, JC Tejada… - Applied Sciences, 2022 - mdpi.com
Currently, research on gesture recognition systems has been on the rise due to the
capabilities these systems provide to the field of human–machine interaction, however …
capabilities these systems provide to the field of human–machine interaction, however …
Attention-based interval aided networks for data modeling of heterogeneous sampling sequences with missing values in process industry
In complex process industries, multivariate time sequences are omnipresent, whose
nonlinearities and dynamics present two major challenges for soft sensing of important …
nonlinearities and dynamics present two major challenges for soft sensing of important …
[HTML][HTML] GBT: Two-stage transformer framework for non-stationary time series forecasting
L Shen, Y Wei, Y Wang - Neural Networks, 2023 - Elsevier
This paper shows that time series forecasting Transformer (TSFT) suffers from severe over-
fitting problem caused by improper initialization method of unknown decoder inputs …
fitting problem caused by improper initialization method of unknown decoder inputs …
Unsupervised deep learning for IoT time series
Internet of Things (IoT) time-series analysis has found numerous applications in a wide
variety of areas, ranging from health informatics to network security. Nevertheless, the …
variety of areas, ranging from health informatics to network security. Nevertheless, the …
Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks
The high concentrations of heavy metals in municipal industrial sewer networks will
seriously impact the microorganisms of the activated sludge in the wastewater treatment …
seriously impact the microorganisms of the activated sludge in the wastewater treatment …