Accurately choosing a model with sequential data
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The dataset I'm working on is mapping journeys - breaking them down into entry & exit coordinates, and entry & exit times, for each part of the journey. My goal is to predict the final exit coordinates, given the final time (though I'm not 100% sure time matters).
I'm having an issue finding an appropriate model that takes the time features into account. At the moment, rather than predicting this final location (x,y coordinate), I'm using a catboost classifier to tell me whether the final location of each user will be in a given area or not, but I'm not sure if I'm barking up the wrong tree. A problem I have is when I flatten the data (which I feel I need to?), I have a lot of NaN values, because each journey is a different number of trajectories added together (up to 20).
I was doing a little research and found some papers on applying neural nets (specifically RNNs) to this kind of data, but my knowledge of NNs is rather incomplete.
What sort of model might I try to better fit my data? Would I be best off getting to grips with RNNs?
machine-learning dataset data-cleaning
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The dataset I'm working on is mapping journeys - breaking them down into entry & exit coordinates, and entry & exit times, for each part of the journey. My goal is to predict the final exit coordinates, given the final time (though I'm not 100% sure time matters).
I'm having an issue finding an appropriate model that takes the time features into account. At the moment, rather than predicting this final location (x,y coordinate), I'm using a catboost classifier to tell me whether the final location of each user will be in a given area or not, but I'm not sure if I'm barking up the wrong tree. A problem I have is when I flatten the data (which I feel I need to?), I have a lot of NaN values, because each journey is a different number of trajectories added together (up to 20).
I was doing a little research and found some papers on applying neural nets (specifically RNNs) to this kind of data, but my knowledge of NNs is rather incomplete.
What sort of model might I try to better fit my data? Would I be best off getting to grips with RNNs?
machine-learning dataset data-cleaning
asked 6 hours ago
A BerryA Berry
11
New contributor
A Berry is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
The dataset I'm working on is mapping journeys - breaking them down into entry & exit coordinates, and entry & exit times, for each part of the journey. My goal is to predict the final exit coordinates, given the final time (though I'm not 100% sure time matters).
I'm having an issue finding an appropriate model that takes the time features into account. At the moment, rather than predicting this final location (x,y coordinate), I'm using a catboost classifier to tell me whether the final location of each user will be in a given area or not, but I'm not sure if I'm barking up the wrong tree. A problem I have is when I flatten the data (which I feel I need to?), I have a lot of NaN values, because each journey is a different number of trajectories added together (up to 20).
I was doing a little research and found some papers on applying neural nets (specifically RNNs) to this kind of data, but my knowledge of NNs is rather incomplete.
What sort of model might I try to better fit my data? Would I be best off getting to grips with RNNs?
machine-learning dataset data-cleaning
asked 6 hours ago
A BerryA Berry
11
New contributor
A Berry is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The dataset I'm working on is mapping journeys - breaking them down into entry & exit coordinates, and entry & exit times, for each part of the journey. My goal is to predict the final exit coordinates, given the final time (though I'm not 100% sure time matters).
I'm having an issue finding an appropriate model that takes the time features into account. At the moment, rather than predicting this final location (x,y coordinate), I'm using a catboost classifier to tell me whether the final location of each user will be in a given area or not, but I'm not sure if I'm barking up the wrong tree. A problem I have is when I flatten the data (which I feel I need to?), I have a lot of NaN values, because each journey is a different number of trajectories added together (up to 20).
I was doing a little research and found some papers on applying neural nets (specifically RNNs) to this kind of data, but my knowledge of NNs is rather incomplete.
What sort of model might I try to better fit my data? Would I be best off getting to grips with RNNs?
machine-learning dataset data-cleaning
machine-learning dataset data-cleaning
asked 6 hours ago
A BerryA Berry
11
New contributor
A Berry is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 hours ago
A BerryA Berry
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edited 6 hours ago
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asked 6 hours ago
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asked 6 hours ago
A BerryA Berry
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asked 6 hours ago
A BerryA Berry
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11
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A Berry is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
A Berry is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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