How to manage missing data in meteorological time series? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsTechniques for dealing with unevenly spaced time series data that have missing time-stamps?ARIMAX v. ARX Time Series ModelingOne-class classifier for time series data classificationHow to cluster multiple time-series from one data frameMultivariate time series classificationPre-processing irregular, high frequency time-series data in pythonTime series forecasting using multiple time series as training dataMultivariate time series classification using KNN and DTWAny thoughts on how to fill missing (isolated, and ranges) annual data to improve accuracy for future predictionsLSTM Time series prediction for multiple multivariate series
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How to manage missing data in meteorological time series?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsTechniques for dealing with unevenly spaced time series data that have missing time-stamps?ARIMAX v. ARX Time Series ModelingOne-class classifier for time series data classificationHow to cluster multiple time-series from one data frameMultivariate time series classificationPre-processing irregular, high frequency time-series data in pythonTime series forecasting using multiple time series as training dataMultivariate time series classification using KNN and DTWAny thoughts on how to fill missing (isolated, and ranges) annual data to improve accuracy for future predictionsLSTM Time series prediction for multiple multivariate series
$begingroup$
How to know the type of missing data is what it is: MCAR, MAR or NMAR, knowing that I'm working on time series multivariate, and is that going to help me deal with the missing data, and what is the best techniques of processing missing data in time series, knowing that I work on meteorological data?
time-series
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
asked Mar 2 at 14:33
Boughrara Boughrara
61
$endgroup$
add a comment |
$begingroup$
How to know the type of missing data is what it is: MCAR, MAR or NMAR, knowing that I'm working on time series multivariate, and is that going to help me deal with the missing data, and what is the best techniques of processing missing data in time series, knowing that I work on meteorological data?
time-series
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
asked Mar 2 at 14:33
Boughrara Boughrara
61
$endgroup$
add a comment |
$begingroup$
How to know the type of missing data is what it is: MCAR, MAR or NMAR, knowing that I'm working on time series multivariate, and is that going to help me deal with the missing data, and what is the best techniques of processing missing data in time series, knowing that I work on meteorological data?
time-series
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
asked Mar 2 at 14:33
Boughrara Boughrara
61
$endgroup$
How to know the type of missing data is what it is: MCAR, MAR or NMAR, knowing that I'm working on time series multivariate, and is that going to help me deal with the missing data, and what is the best techniques of processing missing data in time series, knowing that I work on meteorological data?
time-series
time-series
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
asked Mar 2 at 14:33
Boughrara Boughrara
61
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
asked Mar 2 at 14:33
Boughrara Boughrara
61
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
edited Apr 2 at 1:16
Stephen Rauch♦
1,52551330
1,52551330
asked Mar 2 at 14:33
Boughrara Boughrara
61
asked Mar 2 at 14:33
Boughrara Boughrara
61
asked Mar 2 at 14:33
Boughrara Boughrara
61
61
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
It is a question related to the domain of your project. You should know the cause of the missingness.
If some values are missing, because there is no applicable measure then this values are a special case, therefore they're missing not at random. In such case you can impute missing values with the mean value of the non-missing data and add another feature, which indicates special cases.
On the other hand, if the value is missing, because some sensor temporarily stopped working, then it is missing at random, so the measured value is just not known. In this situation you can perform linear regression (or any other regression, but you should start with the linear one) between non-missing data.
In case your problem has mixed types of missingness you should also perform some regression.
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
$endgroup$
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
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votes
$begingroup$
It is a question related to the domain of your project. You should know the cause of the missingness.
If some values are missing, because there is no applicable measure then this values are a special case, therefore they're missing not at random. In such case you can impute missing values with the mean value of the non-missing data and add another feature, which indicates special cases.
On the other hand, if the value is missing, because some sensor temporarily stopped working, then it is missing at random, so the measured value is just not known. In this situation you can perform linear regression (or any other regression, but you should start with the linear one) between non-missing data.
In case your problem has mixed types of missingness you should also perform some regression.
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
$endgroup$
add a comment |
$begingroup$
It is a question related to the domain of your project. You should know the cause of the missingness.
If some values are missing, because there is no applicable measure then this values are a special case, therefore they're missing not at random. In such case you can impute missing values with the mean value of the non-missing data and add another feature, which indicates special cases.
On the other hand, if the value is missing, because some sensor temporarily stopped working, then it is missing at random, so the measured value is just not known. In this situation you can perform linear regression (or any other regression, but you should start with the linear one) between non-missing data.
In case your problem has mixed types of missingness you should also perform some regression.
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
$endgroup$
add a comment |
$begingroup$
It is a question related to the domain of your project. You should know the cause of the missingness.
If some values are missing, because there is no applicable measure then this values are a special case, therefore they're missing not at random. In such case you can impute missing values with the mean value of the non-missing data and add another feature, which indicates special cases.
On the other hand, if the value is missing, because some sensor temporarily stopped working, then it is missing at random, so the measured value is just not known. In this situation you can perform linear regression (or any other regression, but you should start with the linear one) between non-missing data.
In case your problem has mixed types of missingness you should also perform some regression.
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
$endgroup$
It is a question related to the domain of your project. You should know the cause of the missingness.
If some values are missing, because there is no applicable measure then this values are a special case, therefore they're missing not at random. In such case you can impute missing values with the mean value of the non-missing data and add another feature, which indicates special cases.
On the other hand, if the value is missing, because some sensor temporarily stopped working, then it is missing at random, so the measured value is just not known. In this situation you can perform linear regression (or any other regression, but you should start with the linear one) between non-missing data.
In case your problem has mixed types of missingness you should also perform some regression.
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
edited Mar 2 at 16:45
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
answered Mar 2 at 16:29
Michał KardachMichał Kardach
716
716
add a comment |
add a comment |
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