Unique ETA prediction vs continuous ETA prediction
$begingroup$
Currently I have a predictive model, which predicts ETA flight times. Specifically, this is a regression that predicts the flight time that results from some features and the target variable:
Flight_duration = Arrival-time – Airborne time
This specified flight time is added to the Airborne timestamp to predict when it should be there.
Now I want to extend the model. It should no longer be predicted once at Airborne, but several times during the flight to improve the ETA forecast.
I got real-time flight data for that. There are now several lines with timestamp + coordinates for a specific flight where it is located.
The question I ask now is, how do I model exactly?
How can I use the coordinates optimally as a feature (as a geohash?)? Should I see this as a time series prediction? For example, I push the last t-3 features..for example geohash and how long the flight_duration to these points was in the model and forecast e.g. always the t + 1 flight duration.
Currently I use a Gradient Boosting model, which is quite good for a forecast at the Airborne time. For example, I have an RMSE of about 8 minutes at 12 hours flight time.
Do you have experience and can you share it with me?
machine-learning time-series
asked 3 mins ago
OrgosMosOrgosMos
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$begingroup$
Currently I have a predictive model, which predicts ETA flight times. Specifically, this is a regression that predicts the flight time that results from some features and the target variable:
Flight_duration = Arrival-time – Airborne time
This specified flight time is added to the Airborne timestamp to predict when it should be there.
Now I want to extend the model. It should no longer be predicted once at Airborne, but several times during the flight to improve the ETA forecast.
I got real-time flight data for that. There are now several lines with timestamp + coordinates for a specific flight where it is located.
The question I ask now is, how do I model exactly?
How can I use the coordinates optimally as a feature (as a geohash?)? Should I see this as a time series prediction? For example, I push the last t-3 features..for example geohash and how long the flight_duration to these points was in the model and forecast e.g. always the t + 1 flight duration.
Currently I use a Gradient Boosting model, which is quite good for a forecast at the Airborne time. For example, I have an RMSE of about 8 minutes at 12 hours flight time.
Do you have experience and can you share it with me?
machine-learning time-series
asked 3 mins ago
OrgosMosOrgosMos
1
New contributor
OrgosMos 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 |
$begingroup$
Currently I have a predictive model, which predicts ETA flight times. Specifically, this is a regression that predicts the flight time that results from some features and the target variable:
Flight_duration = Arrival-time – Airborne time
This specified flight time is added to the Airborne timestamp to predict when it should be there.
Now I want to extend the model. It should no longer be predicted once at Airborne, but several times during the flight to improve the ETA forecast.
I got real-time flight data for that. There are now several lines with timestamp + coordinates for a specific flight where it is located.
The question I ask now is, how do I model exactly?
How can I use the coordinates optimally as a feature (as a geohash?)? Should I see this as a time series prediction? For example, I push the last t-3 features..for example geohash and how long the flight_duration to these points was in the model and forecast e.g. always the t + 1 flight duration.
Currently I use a Gradient Boosting model, which is quite good for a forecast at the Airborne time. For example, I have an RMSE of about 8 minutes at 12 hours flight time.
Do you have experience and can you share it with me?
machine-learning time-series
asked 3 mins ago
OrgosMosOrgosMos
1
New contributor
OrgosMos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Currently I have a predictive model, which predicts ETA flight times. Specifically, this is a regression that predicts the flight time that results from some features and the target variable:
Flight_duration = Arrival-time – Airborne time
This specified flight time is added to the Airborne timestamp to predict when it should be there.
Now I want to extend the model. It should no longer be predicted once at Airborne, but several times during the flight to improve the ETA forecast.
I got real-time flight data for that. There are now several lines with timestamp + coordinates for a specific flight where it is located.
The question I ask now is, how do I model exactly?
How can I use the coordinates optimally as a feature (as a geohash?)? Should I see this as a time series prediction? For example, I push the last t-3 features..for example geohash and how long the flight_duration to these points was in the model and forecast e.g. always the t + 1 flight duration.
Currently I use a Gradient Boosting model, which is quite good for a forecast at the Airborne time. For example, I have an RMSE of about 8 minutes at 12 hours flight time.
Do you have experience and can you share it with me?
machine-learning time-series
machine-learning time-series
asked 3 mins ago
OrgosMosOrgosMos
1
New contributor
OrgosMos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
OrgosMosOrgosMos
1
New contributor
OrgosMos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 3 mins ago
OrgosMosOrgosMos
1
New contributor
OrgosMos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
OrgosMosOrgosMos
1
asked 3 mins ago
OrgosMosOrgosMos
1
1
New contributor
OrgosMos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
OrgosMos 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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