How to reshape data for LSTM training in multivariate sequence prediction
$begingroup$
I want to build an LSTM model for customer behaviour. It's the first time for me working on a timeseries, so some concepts are not clear to me at all.
My prediction problem is multidimensional, meaning that I also want to predict many informations associated to an action for each customer.
The dataset is currently shaped as a list of 2d padded arrays of one-hot encoded features (customer actions + other informations), for example:
customer_id encoded_features
0 25464205 [[0,1,0],..,[1,1,1],[1,0,1],..,[1,0,1]]
1 56456574 [[0,1,1],..,[1,0,1],[1,0,1],..,[1,1,1]]
where each element in the encoded_features entries represents a specific timestep.
My idea here is to use keras input shape
(n. customers, n. timesteps, length of features encoding)
In the example above it would be (2,#timesteps,3).
I have two main questions:
Is this whole setting rigth for the prediction of next single customer action? I would like to simply give a new sequence of features for a certain customer and predict all features in the next timestep.
I am thinking about splitting the data (according to a certain ratio) into sequential training and test sets, in order to test the trained model on unseen feature vectors. In the example above it would be:
customer_id X_train y_train
0 25464205 [[0,1,0],..] [1,1,1]
1 56456574 [[0,1,1],..] [1,0,1]
customer_id X_test y_test
0 25464205 [[1,0,1],..] [1,0,1]
1 56456574 [[1,0,1],..] [1,1,1]
Notice that X_train and X_test will generally contain all Train/Test events, except for the last one which has to be predicted.
Is this a correct interpretation?
python keras time-series lstm recurrent-neural-net
asked yesterday
ginevracoalginevracoal
1085
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add a comment |
$begingroup$
I want to build an LSTM model for customer behaviour. It's the first time for me working on a timeseries, so some concepts are not clear to me at all.
My prediction problem is multidimensional, meaning that I also want to predict many informations associated to an action for each customer.
The dataset is currently shaped as a list of 2d padded arrays of one-hot encoded features (customer actions + other informations), for example:
customer_id encoded_features
0 25464205 [[0,1,0],..,[1,1,1],[1,0,1],..,[1,0,1]]
1 56456574 [[0,1,1],..,[1,0,1],[1,0,1],..,[1,1,1]]
where each element in the encoded_features entries represents a specific timestep.
My idea here is to use keras input shape
(n. customers, n. timesteps, length of features encoding)
In the example above it would be (2,#timesteps,3).
I have two main questions:
Is this whole setting rigth for the prediction of next single customer action? I would like to simply give a new sequence of features for a certain customer and predict all features in the next timestep.
I am thinking about splitting the data (according to a certain ratio) into sequential training and test sets, in order to test the trained model on unseen feature vectors. In the example above it would be:
customer_id X_train y_train
0 25464205 [[0,1,0],..] [1,1,1]
1 56456574 [[0,1,1],..] [1,0,1]
customer_id X_test y_test
0 25464205 [[1,0,1],..] [1,0,1]
1 56456574 [[1,0,1],..] [1,1,1]
Notice that X_train and X_test will generally contain all Train/Test events, except for the last one which has to be predicted.
Is this a correct interpretation?
python keras time-series lstm recurrent-neural-net
asked yesterday
ginevracoalginevracoal
1085
New contributor
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
Test / Train split seems odd. For training, model will see just 1 record for each customer id. Is that what you intend to do ?
$endgroup$
– Shamit Verma
yesterday
$begingroup$
My fault, I just edited the question. I hope it's clearer now.
$endgroup$
– ginevracoal
yesterday
add a comment |
$begingroup$
I want to build an LSTM model for customer behaviour. It's the first time for me working on a timeseries, so some concepts are not clear to me at all.
My prediction problem is multidimensional, meaning that I also want to predict many informations associated to an action for each customer.
The dataset is currently shaped as a list of 2d padded arrays of one-hot encoded features (customer actions + other informations), for example:
customer_id encoded_features
0 25464205 [[0,1,0],..,[1,1,1],[1,0,1],..,[1,0,1]]
1 56456574 [[0,1,1],..,[1,0,1],[1,0,1],..,[1,1,1]]
where each element in the encoded_features entries represents a specific timestep.
My idea here is to use keras input shape
(n. customers, n. timesteps, length of features encoding)
In the example above it would be (2,#timesteps,3).
I have two main questions:
Is this whole setting rigth for the prediction of next single customer action? I would like to simply give a new sequence of features for a certain customer and predict all features in the next timestep.
I am thinking about splitting the data (according to a certain ratio) into sequential training and test sets, in order to test the trained model on unseen feature vectors. In the example above it would be:
customer_id X_train y_train
0 25464205 [[0,1,0],..] [1,1,1]
1 56456574 [[0,1,1],..] [1,0,1]
customer_id X_test y_test
0 25464205 [[1,0,1],..] [1,0,1]
1 56456574 [[1,0,1],..] [1,1,1]
Notice that X_train and X_test will generally contain all Train/Test events, except for the last one which has to be predicted.
Is this a correct interpretation?
python keras time-series lstm recurrent-neural-net
asked yesterday
ginevracoalginevracoal
1085
New contributor
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I want to build an LSTM model for customer behaviour. It's the first time for me working on a timeseries, so some concepts are not clear to me at all.
My prediction problem is multidimensional, meaning that I also want to predict many informations associated to an action for each customer.
The dataset is currently shaped as a list of 2d padded arrays of one-hot encoded features (customer actions + other informations), for example:
customer_id encoded_features
0 25464205 [[0,1,0],..,[1,1,1],[1,0,1],..,[1,0,1]]
1 56456574 [[0,1,1],..,[1,0,1],[1,0,1],..,[1,1,1]]
where each element in the encoded_features entries represents a specific timestep.
My idea here is to use keras input shape
(n. customers, n. timesteps, length of features encoding)
In the example above it would be (2,#timesteps,3).
I have two main questions:
Is this whole setting rigth for the prediction of next single customer action? I would like to simply give a new sequence of features for a certain customer and predict all features in the next timestep.
I am thinking about splitting the data (according to a certain ratio) into sequential training and test sets, in order to test the trained model on unseen feature vectors. In the example above it would be:
customer_id X_train y_train
0 25464205 [[0,1,0],..] [1,1,1]
1 56456574 [[0,1,1],..] [1,0,1]
customer_id X_test y_test
0 25464205 [[1,0,1],..] [1,0,1]
1 56456574 [[1,0,1],..] [1,1,1]
Notice that X_train and X_test will generally contain all Train/Test events, except for the last one which has to be predicted.
Is this a correct interpretation?
python keras time-series lstm recurrent-neural-net
python keras time-series lstm recurrent-neural-net
asked yesterday
ginevracoalginevracoal
1085
New contributor
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
ginevracoalginevracoal
1085
New contributor
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
ginevracoal
asked yesterday
ginevracoalginevracoal
1085
New contributor
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
ginevracoalginevracoal
1085
asked yesterday
ginevracoalginevracoal
1085
1085
New contributor
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
ginevracoal is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
1
$begingroup$
Test / Train split seems odd. For training, model will see just 1 record for each customer id. Is that what you intend to do ?
$endgroup$
– Shamit Verma
yesterday
$begingroup$
My fault, I just edited the question. I hope it's clearer now.
$endgroup$
– ginevracoal
yesterday
add a comment |
1
$begingroup$
Test / Train split seems odd. For training, model will see just 1 record for each customer id. Is that what you intend to do ?
$endgroup$
– Shamit Verma
yesterday
$begingroup$
My fault, I just edited the question. I hope it's clearer now.
$endgroup$
– ginevracoal
yesterday
1
1
$begingroup$
Test / Train split seems odd. For training, model will see just 1 record for each customer id. Is that what you intend to do ?
$endgroup$
– Shamit Verma
yesterday
$begingroup$
Test / Train split seems odd. For training, model will see just 1 record for each customer id. Is that what you intend to do ?
$endgroup$
– Shamit Verma
yesterday
$begingroup$
My fault, I just edited the question. I hope it's clearer now.
$endgroup$
– ginevracoal
yesterday
$begingroup$
My fault, I just edited the question. I hope it's clearer now.
$endgroup$
– ginevracoal
yesterday
add a comment |
1 Answer
1
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oldest
votes
$begingroup$
This makes sense. It should work for input and first couple of layers. For output layers, you can have a softmax if you need to generate only next record in sequence.
Following Keras code has an example that :
- Accepts multi-dimensional inputs (Each sample is a Sequence of video frames)
- Predicts next few frames of video ( Multi dimensional since each pixel is a feature)
https://github.com/keras-team/keras/blob/master/examples/conv_lstm.py
answered yesterday
Shamit VermaShamit Verma
57516
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add a comment |
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1 Answer
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$begingroup$
This makes sense. It should work for input and first couple of layers. For output layers, you can have a softmax if you need to generate only next record in sequence.
Following Keras code has an example that :
- Accepts multi-dimensional inputs (Each sample is a Sequence of video frames)
- Predicts next few frames of video ( Multi dimensional since each pixel is a feature)
https://github.com/keras-team/keras/blob/master/examples/conv_lstm.py
answered yesterday
Shamit VermaShamit Verma
57516
$endgroup$
add a comment |
$begingroup$
This makes sense. It should work for input and first couple of layers. For output layers, you can have a softmax if you need to generate only next record in sequence.
Following Keras code has an example that :
- Accepts multi-dimensional inputs (Each sample is a Sequence of video frames)
- Predicts next few frames of video ( Multi dimensional since each pixel is a feature)
https://github.com/keras-team/keras/blob/master/examples/conv_lstm.py
answered yesterday
Shamit VermaShamit Verma
57516
$endgroup$
add a comment |
$begingroup$
This makes sense. It should work for input and first couple of layers. For output layers, you can have a softmax if you need to generate only next record in sequence.
Following Keras code has an example that :
- Accepts multi-dimensional inputs (Each sample is a Sequence of video frames)
- Predicts next few frames of video ( Multi dimensional since each pixel is a feature)
https://github.com/keras-team/keras/blob/master/examples/conv_lstm.py
answered yesterday
Shamit VermaShamit Verma
57516
$endgroup$
This makes sense. It should work for input and first couple of layers. For output layers, you can have a softmax if you need to generate only next record in sequence.
Following Keras code has an example that :
- Accepts multi-dimensional inputs (Each sample is a Sequence of video frames)
- Predicts next few frames of video ( Multi dimensional since each pixel is a feature)
https://github.com/keras-team/keras/blob/master/examples/conv_lstm.py
answered yesterday
Shamit VermaShamit Verma
57516
answered yesterday
Shamit VermaShamit Verma
57516
answered yesterday
Shamit VermaShamit Verma
57516
answered yesterday
Shamit VermaShamit Verma
57516
57516
add a comment |
add a comment |
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1
$begingroup$
Test / Train split seems odd. For training, model will see just 1 record for each customer id. Is that what you intend to do ?
$endgroup$
– Shamit Verma
yesterday
$begingroup$
My fault, I just edited the question. I hope it's clearer now.
$endgroup$
– ginevracoal
yesterday