Feature selection for time series prediction
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I'm working on an LSTM-based stock market forecasting problem and trying to figure out a way to select input variables.
When calculating correlation between variables (e.g. Close price of Tesla vs Close price of Microsoft), would differentiating the curves give a more accurate (or correct) correlation index ? I'm finding values in the range 0.7-0.9 for non-differentiated variables, and lower values after differentiation.
Once I have a correlation matrix of all my variables, is there a way to figure out which ones would add information to the neural net and which ones would just add noise ?
time-series feature-selection correlation
asked Aug 12 '18 at 14:07
MOffMOff
161
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This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
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$begingroup$
I'm working on an LSTM-based stock market forecasting problem and trying to figure out a way to select input variables.
When calculating correlation between variables (e.g. Close price of Tesla vs Close price of Microsoft), would differentiating the curves give a more accurate (or correct) correlation index ? I'm finding values in the range 0.7-0.9 for non-differentiated variables, and lower values after differentiation.
Once I have a correlation matrix of all my variables, is there a way to figure out which ones would add information to the neural net and which ones would just add noise ?
time-series feature-selection correlation
asked Aug 12 '18 at 14:07
MOffMOff
161
$endgroup$
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm working on an LSTM-based stock market forecasting problem and trying to figure out a way to select input variables.
When calculating correlation between variables (e.g. Close price of Tesla vs Close price of Microsoft), would differentiating the curves give a more accurate (or correct) correlation index ? I'm finding values in the range 0.7-0.9 for non-differentiated variables, and lower values after differentiation.
Once I have a correlation matrix of all my variables, is there a way to figure out which ones would add information to the neural net and which ones would just add noise ?
time-series feature-selection correlation
asked Aug 12 '18 at 14:07
MOffMOff
161
$endgroup$
I'm working on an LSTM-based stock market forecasting problem and trying to figure out a way to select input variables.
When calculating correlation between variables (e.g. Close price of Tesla vs Close price of Microsoft), would differentiating the curves give a more accurate (or correct) correlation index ? I'm finding values in the range 0.7-0.9 for non-differentiated variables, and lower values after differentiation.
Once I have a correlation matrix of all my variables, is there a way to figure out which ones would add information to the neural net and which ones would just add noise ?
time-series feature-selection correlation
time-series feature-selection correlation
asked Aug 12 '18 at 14:07
MOffMOff
161
asked Aug 12 '18 at 14:07
MOffMOff
161
asked Aug 12 '18 at 14:07
MOffMOff
161
asked Aug 12 '18 at 14:07
MOffMOff
161
asked Aug 12 '18 at 14:07
MOffMOff
161
161
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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You don’t need to select variables for feeding to network, deep neural networks (DNN) will do this automatically. Actually DNN gives more importance to relevant variables by setting its weights. After setting the weights, some of the hidden nodes take 0 and some of them take 1 (because of sigmoid function). You can think of this 1 and 0’s as choosing relevant variables, too.
By the way, correlation matrix can not be used to select relevant variables directly. If you want to reduce the number of variables that are fed to DNN, you can use PCA. Actually PCA components are calculated by getting the Eigen-vectors of correlation matrix.
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
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$begingroup$
You don’t need to select variables for feeding to network, deep neural networks (DNN) will do this automatically. Actually DNN gives more importance to relevant variables by setting its weights. After setting the weights, some of the hidden nodes take 0 and some of them take 1 (because of sigmoid function). You can think of this 1 and 0’s as choosing relevant variables, too.
By the way, correlation matrix can not be used to select relevant variables directly. If you want to reduce the number of variables that are fed to DNN, you can use PCA. Actually PCA components are calculated by getting the Eigen-vectors of correlation matrix.
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
$endgroup$
add a comment |
$begingroup$
You don’t need to select variables for feeding to network, deep neural networks (DNN) will do this automatically. Actually DNN gives more importance to relevant variables by setting its weights. After setting the weights, some of the hidden nodes take 0 and some of them take 1 (because of sigmoid function). You can think of this 1 and 0’s as choosing relevant variables, too.
By the way, correlation matrix can not be used to select relevant variables directly. If you want to reduce the number of variables that are fed to DNN, you can use PCA. Actually PCA components are calculated by getting the Eigen-vectors of correlation matrix.
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
$endgroup$
add a comment |
$begingroup$
You don’t need to select variables for feeding to network, deep neural networks (DNN) will do this automatically. Actually DNN gives more importance to relevant variables by setting its weights. After setting the weights, some of the hidden nodes take 0 and some of them take 1 (because of sigmoid function). You can think of this 1 and 0’s as choosing relevant variables, too.
By the way, correlation matrix can not be used to select relevant variables directly. If you want to reduce the number of variables that are fed to DNN, you can use PCA. Actually PCA components are calculated by getting the Eigen-vectors of correlation matrix.
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
$endgroup$
You don’t need to select variables for feeding to network, deep neural networks (DNN) will do this automatically. Actually DNN gives more importance to relevant variables by setting its weights. After setting the weights, some of the hidden nodes take 0 and some of them take 1 (because of sigmoid function). You can think of this 1 and 0’s as choosing relevant variables, too.
By the way, correlation matrix can not be used to select relevant variables directly. If you want to reduce the number of variables that are fed to DNN, you can use PCA. Actually PCA components are calculated by getting the Eigen-vectors of correlation matrix.
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
edited Aug 12 '18 at 16:42
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
answered Aug 12 '18 at 16:37
pythinkerpythinker
7581212
7581212
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