Specific data formatting techniques for discontiguous time series?
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I'm facing a predicting problem for food alerts. The goal is to predict the variables of the most probable alert in the next x days (also any information I could get about future alerts is really useful for me).
Here is a sample of the dataset that I'm using, where alerts are recorded over time (so it's a time series problem): 
The problem is that observations are not uniform over time (not separated by equal time lapses), i.e: since alerts are only recorded when they happen, there can be one day without alerts and another with 50 alerts.
The entry for the possible model could be the alerts (each alert correctly coded as they are categorical variables) of the last x days, but this entry must have a fixed size/format. Since the time windows don't have the same number of alerts, I don't know what is the correct way to deal with this problem.
Any data formatting suggestion to make the observations uniform over time?
Or should I just face the problem in a different way (different inputs)?
Thanks.
machine-learning time-series predictive-modeling preprocessing forecast
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
$endgroup$
add a comment |
$begingroup$
I'm facing a predicting problem for food alerts. The goal is to predict the variables of the most probable alert in the next x days (also any information I could get about future alerts is really useful for me).
Here is a sample of the dataset that I'm using, where alerts are recorded over time (so it's a time series problem):
The problem is that observations are not uniform over time (not separated by equal time lapses), i.e: since alerts are only recorded when they happen, there can be one day without alerts and another with 50 alerts.
The entry for the possible model could be the alerts (each alert correctly coded as they are categorical variables) of the last x days, but this entry must have a fixed size/format. Since the time windows don't have the same number of alerts, I don't know what is the correct way to deal with this problem.
Any data formatting suggestion to make the observations uniform over time?
Or should I just face the problem in a different way (different inputs)?
Thanks.
machine-learning time-series predictive-modeling preprocessing forecast
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
$endgroup$
add a comment |
$begingroup$
I'm facing a predicting problem for food alerts. The goal is to predict the variables of the most probable alert in the next x days (also any information I could get about future alerts is really useful for me).
Here is a sample of the dataset that I'm using, where alerts are recorded over time (so it's a time series problem):
The problem is that observations are not uniform over time (not separated by equal time lapses), i.e: since alerts are only recorded when they happen, there can be one day without alerts and another with 50 alerts.
The entry for the possible model could be the alerts (each alert correctly coded as they are categorical variables) of the last x days, but this entry must have a fixed size/format. Since the time windows don't have the same number of alerts, I don't know what is the correct way to deal with this problem.
Any data formatting suggestion to make the observations uniform over time?
Or should I just face the problem in a different way (different inputs)?
Thanks.
machine-learning time-series predictive-modeling preprocessing forecast
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
$endgroup$
I'm facing a predicting problem for food alerts. The goal is to predict the variables of the most probable alert in the next x days (also any information I could get about future alerts is really useful for me).
Here is a sample of the dataset that I'm using, where alerts are recorded over time (so it's a time series problem):
The problem is that observations are not uniform over time (not separated by equal time lapses), i.e: since alerts are only recorded when they happen, there can be one day without alerts and another with 50 alerts.
The entry for the possible model could be the alerts (each alert correctly coded as they are categorical variables) of the last x days, but this entry must have a fixed size/format. Since the time windows don't have the same number of alerts, I don't know what is the correct way to deal with this problem.
Any data formatting suggestion to make the observations uniform over time?
Or should I just face the problem in a different way (different inputs)?
Thanks.
machine-learning time-series predictive-modeling preprocessing forecast
machine-learning time-series predictive-modeling preprocessing forecast
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
asked 14 hours ago
Rodrigo DíazRodrigo Díaz
62
62
add a comment |
add a comment |
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