De-noising/removing measurement error from time series with very few observations
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So, I have a series of very small independent time series (4 or 8 observations), each measured with some potential error, the direction of which is indeterminate a priori.
I need to smoothen this series out, with constraints. The constraint is simple: each individual series has to sum to the same value before & after the correction.
I've done this in a rather hacky way using a custom objective function whose minimization yields the requisite set of points. However, the methodology is not really statistically sound, and I want to try and redo the analysis with better technique.
Could somebody points me towards what class of models or techniques or ideas I should be looking at for this problem? So far, I'm thinking of trying out some sort of polynomial curve fitting, but even there I'm not entirely certain of how to best choose between degrees, since creating train & test sets will remove at least 25% of the data from the n = 4 time series (a majority).
Any help would be appreciated!
time-series
asked 7 mins ago
FinebalanceFinebalance
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$begingroup$
So, I have a series of very small independent time series (4 or 8 observations), each measured with some potential error, the direction of which is indeterminate a priori.
I need to smoothen this series out, with constraints. The constraint is simple: each individual series has to sum to the same value before & after the correction.
I've done this in a rather hacky way using a custom objective function whose minimization yields the requisite set of points. However, the methodology is not really statistically sound, and I want to try and redo the analysis with better technique.
Could somebody points me towards what class of models or techniques or ideas I should be looking at for this problem? So far, I'm thinking of trying out some sort of polynomial curve fitting, but even there I'm not entirely certain of how to best choose between degrees, since creating train & test sets will remove at least 25% of the data from the n = 4 time series (a majority).
Any help would be appreciated!
time-series
asked 7 mins ago
FinebalanceFinebalance
1
New contributor
Finebalance is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
So, I have a series of very small independent time series (4 or 8 observations), each measured with some potential error, the direction of which is indeterminate a priori.
I need to smoothen this series out, with constraints. The constraint is simple: each individual series has to sum to the same value before & after the correction.
I've done this in a rather hacky way using a custom objective function whose minimization yields the requisite set of points. However, the methodology is not really statistically sound, and I want to try and redo the analysis with better technique.
Could somebody points me towards what class of models or techniques or ideas I should be looking at for this problem? So far, I'm thinking of trying out some sort of polynomial curve fitting, but even there I'm not entirely certain of how to best choose between degrees, since creating train & test sets will remove at least 25% of the data from the n = 4 time series (a majority).
Any help would be appreciated!
time-series
asked 7 mins ago
FinebalanceFinebalance
1
New contributor
Finebalance is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
So, I have a series of very small independent time series (4 or 8 observations), each measured with some potential error, the direction of which is indeterminate a priori.
I need to smoothen this series out, with constraints. The constraint is simple: each individual series has to sum to the same value before & after the correction.
I've done this in a rather hacky way using a custom objective function whose minimization yields the requisite set of points. However, the methodology is not really statistically sound, and I want to try and redo the analysis with better technique.
Could somebody points me towards what class of models or techniques or ideas I should be looking at for this problem? So far, I'm thinking of trying out some sort of polynomial curve fitting, but even there I'm not entirely certain of how to best choose between degrees, since creating train & test sets will remove at least 25% of the data from the n = 4 time series (a majority).
Any help would be appreciated!
time-series
time-series
asked 7 mins ago
FinebalanceFinebalance
1
New contributor
Finebalance is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
FinebalanceFinebalance
1
New contributor
Finebalance is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
FinebalanceFinebalance
1
New contributor
Finebalance is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
FinebalanceFinebalance
1
asked 7 mins ago
FinebalanceFinebalance
1
1
New contributor
Finebalance is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Finebalance is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Finebalance 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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