LSTM Time series prediction for multiple multivariate series
$begingroup$
I have to predict next min traffic for multiple cities (100+). I am thinking of using LSTM. My main concern is how do I scale the number of cities. How does LSTM learn different amount of traffic and other related features of all cities to predict the next state. What should be the network architecture for such cases.
I was thinking of the following process:
- Normalisation of the data with city specific Min,Max scalar
- Feed sliding window data(t_1 to t_60) to LSTM and predict (t+1) value
- Take the output value and get the actuals values from step 1.
I have read multiple papers and blogs online but mostly the deal with one multivariate time-series. But, in my case its multiple multivariate time series but one generalised model. Can someone suggest what are common industry practises for these and related papers/blogs. Do I need to model for each city (Its not possible in my case because of scalability issues?
time-series lstm scalability
$endgroup$
add a comment |
$begingroup$
I have to predict next min traffic for multiple cities (100+). I am thinking of using LSTM. My main concern is how do I scale the number of cities. How does LSTM learn different amount of traffic and other related features of all cities to predict the next state. What should be the network architecture for such cases.
I was thinking of the following process:
- Normalisation of the data with city specific Min,Max scalar
- Feed sliding window data(t_1 to t_60) to LSTM and predict (t+1) value
- Take the output value and get the actuals values from step 1.
I have read multiple papers and blogs online but mostly the deal with one multivariate time-series. But, in my case its multiple multivariate time series but one generalised model. Can someone suggest what are common industry practises for these and related papers/blogs. Do I need to model for each city (Its not possible in my case because of scalability issues?
time-series lstm scalability
$endgroup$
add a comment |
$begingroup$
I have to predict next min traffic for multiple cities (100+). I am thinking of using LSTM. My main concern is how do I scale the number of cities. How does LSTM learn different amount of traffic and other related features of all cities to predict the next state. What should be the network architecture for such cases.
I was thinking of the following process:
- Normalisation of the data with city specific Min,Max scalar
- Feed sliding window data(t_1 to t_60) to LSTM and predict (t+1) value
- Take the output value and get the actuals values from step 1.
I have read multiple papers and blogs online but mostly the deal with one multivariate time-series. But, in my case its multiple multivariate time series but one generalised model. Can someone suggest what are common industry practises for these and related papers/blogs. Do I need to model for each city (Its not possible in my case because of scalability issues?
time-series lstm scalability
$endgroup$
I have to predict next min traffic for multiple cities (100+). I am thinking of using LSTM. My main concern is how do I scale the number of cities. How does LSTM learn different amount of traffic and other related features of all cities to predict the next state. What should be the network architecture for such cases.
I was thinking of the following process:
- Normalisation of the data with city specific Min,Max scalar
- Feed sliding window data(t_1 to t_60) to LSTM and predict (t+1) value
- Take the output value and get the actuals values from step 1.
I have read multiple papers and blogs online but mostly the deal with one multivariate time-series. But, in my case its multiple multivariate time series but one generalised model. Can someone suggest what are common industry practises for these and related papers/blogs. Do I need to model for each city (Its not possible in my case because of scalability issues?
time-series lstm scalability
time-series lstm scalability
asked yesterday
maggsmaggs
9019
9019
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
I think one question you need to answer is how the traffic is correlated across cities.
If they are not correlated (which is likely) then the city can be a categorical input variable. The network can learn what is common across all cities and what is specific to different cities.
Here is a reference that may be helpful. In this example, the inputs are clearly related, so that may be a difference in your case.
You also may consider developing a model for one city, and then improving it by adding other cities.
New contributor
$endgroup$
$begingroup$
Thanks Steven. I have 100+ cities and corresponding 100+ zones for each. So If i try one-hot encoding, 10000 features are added to the network, which makes it quite slow. I am not sure how single network fits all strategy would work. Can you suggest some references.
$endgroup$
– maggs
22 hours ago
$begingroup$
Sorry, I don't have any reference. The above is based on my idea of how NN learning works. I think the zones within a city are correlated, so you can one-hot encode the cities, and have a zone id column.
$endgroup$
– B Seven
15 hours ago
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f45882%2flstm-time-series-prediction-for-multiple-multivariate-series%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
I think one question you need to answer is how the traffic is correlated across cities.
If they are not correlated (which is likely) then the city can be a categorical input variable. The network can learn what is common across all cities and what is specific to different cities.
Here is a reference that may be helpful. In this example, the inputs are clearly related, so that may be a difference in your case.
You also may consider developing a model for one city, and then improving it by adding other cities.
New contributor
$endgroup$
$begingroup$
Thanks Steven. I have 100+ cities and corresponding 100+ zones for each. So If i try one-hot encoding, 10000 features are added to the network, which makes it quite slow. I am not sure how single network fits all strategy would work. Can you suggest some references.
$endgroup$
– maggs
22 hours ago
$begingroup$
Sorry, I don't have any reference. The above is based on my idea of how NN learning works. I think the zones within a city are correlated, so you can one-hot encode the cities, and have a zone id column.
$endgroup$
– B Seven
15 hours ago
add a comment |
$begingroup$
I think one question you need to answer is how the traffic is correlated across cities.
If they are not correlated (which is likely) then the city can be a categorical input variable. The network can learn what is common across all cities and what is specific to different cities.
Here is a reference that may be helpful. In this example, the inputs are clearly related, so that may be a difference in your case.
You also may consider developing a model for one city, and then improving it by adding other cities.
New contributor
$endgroup$
$begingroup$
Thanks Steven. I have 100+ cities and corresponding 100+ zones for each. So If i try one-hot encoding, 10000 features are added to the network, which makes it quite slow. I am not sure how single network fits all strategy would work. Can you suggest some references.
$endgroup$
– maggs
22 hours ago
$begingroup$
Sorry, I don't have any reference. The above is based on my idea of how NN learning works. I think the zones within a city are correlated, so you can one-hot encode the cities, and have a zone id column.
$endgroup$
– B Seven
15 hours ago
add a comment |
$begingroup$
I think one question you need to answer is how the traffic is correlated across cities.
If they are not correlated (which is likely) then the city can be a categorical input variable. The network can learn what is common across all cities and what is specific to different cities.
Here is a reference that may be helpful. In this example, the inputs are clearly related, so that may be a difference in your case.
You also may consider developing a model for one city, and then improving it by adding other cities.
New contributor
$endgroup$
I think one question you need to answer is how the traffic is correlated across cities.
If they are not correlated (which is likely) then the city can be a categorical input variable. The network can learn what is common across all cities and what is specific to different cities.
Here is a reference that may be helpful. In this example, the inputs are clearly related, so that may be a difference in your case.
You also may consider developing a model for one city, and then improving it by adding other cities.
New contributor
New contributor
answered yesterday
B SevenB Seven
1465
1465
New contributor
New contributor
$begingroup$
Thanks Steven. I have 100+ cities and corresponding 100+ zones for each. So If i try one-hot encoding, 10000 features are added to the network, which makes it quite slow. I am not sure how single network fits all strategy would work. Can you suggest some references.
$endgroup$
– maggs
22 hours ago
$begingroup$
Sorry, I don't have any reference. The above is based on my idea of how NN learning works. I think the zones within a city are correlated, so you can one-hot encode the cities, and have a zone id column.
$endgroup$
– B Seven
15 hours ago
add a comment |
$begingroup$
Thanks Steven. I have 100+ cities and corresponding 100+ zones for each. So If i try one-hot encoding, 10000 features are added to the network, which makes it quite slow. I am not sure how single network fits all strategy would work. Can you suggest some references.
$endgroup$
– maggs
22 hours ago
$begingroup$
Sorry, I don't have any reference. The above is based on my idea of how NN learning works. I think the zones within a city are correlated, so you can one-hot encode the cities, and have a zone id column.
$endgroup$
– B Seven
15 hours ago
$begingroup$
Thanks Steven. I have 100+ cities and corresponding 100+ zones for each. So If i try one-hot encoding, 10000 features are added to the network, which makes it quite slow. I am not sure how single network fits all strategy would work. Can you suggest some references.
$endgroup$
– maggs
22 hours ago
$begingroup$
Thanks Steven. I have 100+ cities and corresponding 100+ zones for each. So If i try one-hot encoding, 10000 features are added to the network, which makes it quite slow. I am not sure how single network fits all strategy would work. Can you suggest some references.
$endgroup$
– maggs
22 hours ago
$begingroup$
Sorry, I don't have any reference. The above is based on my idea of how NN learning works. I think the zones within a city are correlated, so you can one-hot encode the cities, and have a zone id column.
$endgroup$
– B Seven
15 hours ago
$begingroup$
Sorry, I don't have any reference. The above is based on my idea of how NN learning works. I think the zones within a city are correlated, so you can one-hot encode the cities, and have a zone id column.
$endgroup$
– B Seven
15 hours ago
add a comment |
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f45882%2flstm-time-series-prediction-for-multiple-multivariate-series%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown