Correlational graph attention-based Long Short-Term Memory network for multivariate time series prediction
S Han, H Dong, X Teng, X Li, X Wang - Applied Soft Computing, 2021 - Elsevier
Multi-variate time series prediction models use the historical information of multiple
exogenous series to predict the future values of the target series. At present, attention-based …
exogenous series to predict the future values of the target series. At present, attention-based …
Graph correlated attention recurrent neural network for multivariate time series forecasting
X Geng, X He, L Xu, J Yu - Information Sciences, 2022 - Elsevier
Multivariate time series (MTS) forecasting is an urgent problem for numerous valuable
applications. At present, attention-based methods can relieve recurrent neural networks' …
applications. At present, attention-based methods can relieve recurrent neural networks' …
A dual-stage attention-based Bi-LSTM network for multivariate time series prediction
In the context of the big data era, time series data present the characteristics of high
dimensionality and nonlinearity, which bring great challenges to the prediction of …
dimensionality and nonlinearity, which bring great challenges to the prediction of …
Cluster-aware attentive convolutional recurrent network for multivariate time-series forecasting
Multivariate time-series (MTS) forecasting plays a crucial role in various real-world
applications, but the complex dependencies between time-series variables (ie, inter-series …
applications, but the complex dependencies between time-series variables (ie, inter-series …
A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction
Multivariate time series (MTS) prediction aims at predicting future time series by extracting
multiple forms of dependencies of past time series. Traditional prediction methods and deep …
multiple forms of dependencies of past time series. Traditional prediction methods and deep …
CATN: Cross attentive tree-aware network for multivariate time series forecasting
Modeling complex hierarchical and grouped feature interaction in the multivariate time
series data is indispensable to comprehend the data dynamics and predicting the future …
series data is indispensable to comprehend the data dynamics and predicting the future …
Multivariate long sequence time-series forecasting using dynamic graph learning
X Wang, Y Wang, J Peng, Z Zhang - Journal of Ambient Intelligence and …, 2023 - Springer
Time series prediction is a subset of temporal data mining, which seeks to forecast its values
in the future by using the accessible historical observations within the specified time periods …
in the future by using the accessible historical observations within the specified time periods …
A combined model for multivariate time series forecasting based on MLP-feedforward attention-LSTM
Y Liu, C Zhao, Y Huang - IEEE Access, 2022 - ieeexplore.ieee.org
Multivariate time series forecasting has very great practical significance for a long time, and
it has been attracting the attention of researchers from a diverse range of fields. However, it …
it has been attracting the attention of researchers from a diverse range of fields. However, it …
Temporal self-attention-based Conv-LSTM network for multivariate time series prediction
E Fu, Y Zhang, F Yang, S Wang - Neurocomputing, 2022 - Elsevier
Time series play an important role in many fields, such as industrial control, automated
monitoring, and weather forecasting. Because there is often more than one variable in reality …
monitoring, and weather forecasting. Because there is often more than one variable in reality …
Multi-scale temporal features extraction based graph convolutional network with attention for multivariate time series prediction
Y Chen, F Ding, L Zhai - Expert Systems with Applications, 2022 - Elsevier
Modeling for multivariate time series have always been a meaningful subject. Multivariate
time series forecasting is a fundamental problem attracting many researchers in various …
time series forecasting is a fundamental problem attracting many researchers in various …
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