Parallel spatio-temporal attention-based TCN for multivariate time series prediction
J Fan, K Zhang, Y Huang, Y Zhu, B Chen - Neural Computing and …, 2023 - Springer
As industrial systems become more complex and monitoring sensors for everything from
surveillance to our health become more ubiquitous, multivariate time series prediction is …
surveillance to our health become more ubiquitous, multivariate time series prediction is …
Deep learning for time series forecasting: Advances and open problems
A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …
phenomenon evolves over time. Time series forecasting, estimating future values of time …
DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction
Y Liu, C Gong, L Yang, Y Chen - Expert Systems with Applications, 2020 - Elsevier
Long-term prediction of multivariate time series is still an important but challenging problem.
The key to solve this problem is capturing (1) the spatial correlations at the same time,(2) the …
The key to solve this problem is capturing (1) the spatial correlations at the same time,(2) the …
Recurrent broad learning systems for time series prediction
The broad learning system (BLS) is an emerging approach for effective and efficient
modeling of complex systems. The inputs are transferred and placed in the feature nodes …
modeling of complex systems. The inputs are transferred and placed in the feature nodes …
LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns
K Bandara, C Bergmeir… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Generating forecasts for time series with multiple seasonal cycles is an important use case
for many industries nowadays. Accounting for the multiseasonal patterns becomes …
for many industries nowadays. Accounting for the multiseasonal patterns becomes …
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Electric load forecasting is essential for the planning and maintenance of power systems.
However, its un-stationary and non-linear properties impose significant difficulties in …
However, its un-stationary and non-linear properties impose significant difficulties in …
A new method for heart rate prediction based on LSTM-BiLSTM-Att
H Lin, S Zhang, Q Li, Y Li, J Li, Y Yang - Measurement, 2023 - Elsevier
This paper proposes a new method for heart rate prediction based on LSTM-BiLSTM-Att
model (Long Short Term Memory, Bidirectional LSTM, Attention Mechanism). In this LSTM …
model (Long Short Term Memory, Bidirectional LSTM, Attention Mechanism). In this LSTM …
Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope
Abstract Graph Convolutional Networks (GCNs), which can model data in non-Euclidean
space, have received extensive attention in multivariate traffic flow forecasting in recent …
space, have received extensive attention in multivariate traffic flow forecasting in recent …
Time series forecasting based on echo state network and empirical wavelet transformation
Echo state network (ESN) is a reservoir computing structure consisting randomly generated
hidden layer which enables a rapid learning and extrapolation process. On the other hand …
hidden layer which enables a rapid learning and extrapolation process. On the other hand …
Nonpooling convolutional neural network forecasting for seasonal time series with trends
S Liu, H Ji, MC Wang - IEEE transactions on neural networks …, 2019 - ieeexplore.ieee.org
This article focuses on a problem important to automatic machine learning: the automatic
processing of a nonpreprocessed time series. The convolutional neural network (CNN) is …
processing of a nonpreprocessed time series. The convolutional neural network (CNN) is …