Tensorized LSTM with adaptive shared memory for learning trends in multivariate time series
A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.
1 . A computer-implemented method executed on a processor for executing a multi-task deep learning model for learning trends in multivariate time series, the method comprising:
collecting multi-variate time series data about healthcare from a plurality of sensors;
jointly learning both local and global contextual features for predicting a trend of the multivariate time series about healthcare by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, the TLASM including multiple layers of parallel sub-networks, a first layer of each sub-network including multiple independent LSTMs followed by multiple layers of multilayer perceptrons (MLPs), and a last layer of a plurality of task-specific MLPs, wherein all the independent LSTMs and the multiple layers of MLPs are shared by all prediction tasks, and a connection between the sub-networks is a weighted average with an attention mechanism; and
employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data about healthcare to model a short-term dependency between local time series data and subsequent trends,
wherein each of the independent LSTMs directly communicates with each MLP of a first layer of MLPs of the multiple layers of MLPs.
2 . The method of claim 1 , wherein the adaptive shared memory models task relatedness that changes over time as a result of the first layer of each sub-network including the independent LSTMs.
3 . The method of claim 1 , wherein the TLASM learns sub-network connections between different layers for information routing.
4 . The method of claim 3 , wherein the TLASM models long-term dependency within a sequence of the historical trends.
5 . The method of claim 4 , wherein a task-specific sub-network is implemented to integrate the long-term dependency and the short-term dependency.
6 . The method of claim 1 , wherein a number of attentions used between a first layer and a second layer equals a number of sub-networks in the second layer.
7 . The method of claim 1 , wherein the attention mechanism is given as:
α
ij
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{
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i
T
tanh
(
V
i
h
t
(
j
)
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}
∑
k
=
1
q
exp
{
w
i
T
tanh
(
V
i
h
t
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k
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,
where w i ∈ d α and V i ∈ d α ×d are parameters and h represent hidden state vectors.
8 . A non-transitory computer-readable storage medium comprising a computer-readable program for executing a multi-task deep learning model for learning trends in multivariate time series, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:
collecting multi-variate time series data about healthcare from a plurality of sensors;
jointly learning both local and global contextual features for predicting a trend of the multivariate time series about healthcare by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, the TLASM including multiple layers of parallel sub-networks, a first layer of each sub-network including multiple independent LSTMs, followed by multiple layers of multilayer perceptrons (MLPs), and a last layer of a plurality of task-specific MLPs, wherein all the independent LSTMs and the multiple layers of MLPs are shared by all prediction tasks, and a connection between the sub-networks is a weighted average with an attention mechanism; and
employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data about healthcare to model a short-term dependency between local time series data and subsequent trends,
wherein each of the independent LSTMs directly communicates with each MLP of a first layer of MLPs of the multiple layers of MLPs.
9 . The non-transitory computer-readable storage medium of claim 8 , wherein the adaptive shared memory models task relatedness that changes over time as a result of the first layer of each sub-network including the independent LSTMs.
10 . The non-transitory computer-readable storage medium of claim 8 , wherein the TLASM learns connections between different layers for information routing.
11 . The non-transitory computer-readable storage medium of claim 10 , wherein the TLASM models optimizes long-term dependency within a sequence of the historical trends.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein a task-specific sub-network is implemented to integrate the long-term dependency and the short-term dependency.
13 . The non-transitory computer-readable storage medium of claim 8 , wherein a number of attentions used between a first layer and a second layer equals to a number of sub-networks in the second layer.
14 . The non-transitory computer-readable storage medium of claim 8 , wherein the attention mechanism is given as:
α
ij
=
exp
{
w
i
T
tanh
(
V
i
h
t
(
j
)
)
}
∑
k
=
1
q
exp
{
w
i
T
tanh
(
V
i
h
t
(
k
)
)
}
,
where w i ∈ d α and V i ∈ d α ×d are parameters and h represent hidden state vectors.
15 . A system for executing a multi-task deep learning model for learning trends in multivariate time series, the system comprising:
a memory; and
one or more processors in communication with the memory configured to:
collect multi-variate time series data about healthcare from a plurality of sensors;
jointly learn both local and global contextual features for predicting a trend of the multivariate time series about healthcare by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, the TLASM including multiple layers of parallel sub-networks, a first layer of each sub-network including multiple independent LSTMs, followed by multiple layers of multilayer perceptions (MLPs), and a last layer of a plurality of task-specific MLPs, wherein all the independent LSTMs and the multiple layers of MLPs are shared by all prediction tasks, and a connection between the sub-networks is a weighted average with an attention mechanism; and
employ a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data about healthcare to model a short-term dependency between local time series data and subsequent trends,
wherein each of the independent LSTMs directly communicates with each MLP of a first layer of MLPs of the multiple layers of MLPs.
16 . The system of claim 15 , wherein the adaptive shared memory models task relatedness that changes over time as a result of the first layer of each sub-network including the independent LSTMs.
17 . The system of claim 15 , wherein the TLASM learns optimizes sub-network connections between different layers for information routing.
18 . The system of claim 17 , wherein the TLASM models long-term dependency within a sequence of the historical trends.
19 . The system of claim 18 , wherein a task-specific sub-network is implemented to integrate the long-term dependency and the short-term dependency.
20 . The system of claim 15 , wherein a number of attentions used between a first layer and a second layer equals to a number of sub-networks in the second layer.