Rate quality of sources












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Sorry if the title is misleading, I don't know how to describe the issue correctly, which also makes it hard to google.



Say we have historical weather predictions from three different sources. All predictions are for the same timestamp. We also have the actual weather at that timestamp, so we can assign a score to the quality of each prediction at each timestamp.



How do we go from this to evaluating new predictions? In other words, given three different weather predictions for tomorrow, which one do I trust?



The naive approach will be to always pick the one which has the best past average score (maybe a sliding window, taking the past year) and dynamically update the scores after each prediction. But I wonder if there is a more elegant method?



Also: What are good scoring methods in this example?
One side condition is that we cannot just average over the predictions, we have to pick one.



I appreciate any links to relevant literature. I looked into consolidation of data from different sources, but they are trying to solve different problems.










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    $begingroup$


    Sorry if the title is misleading, I don't know how to describe the issue correctly, which also makes it hard to google.



    Say we have historical weather predictions from three different sources. All predictions are for the same timestamp. We also have the actual weather at that timestamp, so we can assign a score to the quality of each prediction at each timestamp.



    How do we go from this to evaluating new predictions? In other words, given three different weather predictions for tomorrow, which one do I trust?



    The naive approach will be to always pick the one which has the best past average score (maybe a sliding window, taking the past year) and dynamically update the scores after each prediction. But I wonder if there is a more elegant method?



    Also: What are good scoring methods in this example?
    One side condition is that we cannot just average over the predictions, we have to pick one.



    I appreciate any links to relevant literature. I looked into consolidation of data from different sources, but they are trying to solve different problems.










    share|improve this question









    New contributor




    Darina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      0












      0








      0





      $begingroup$


      Sorry if the title is misleading, I don't know how to describe the issue correctly, which also makes it hard to google.



      Say we have historical weather predictions from three different sources. All predictions are for the same timestamp. We also have the actual weather at that timestamp, so we can assign a score to the quality of each prediction at each timestamp.



      How do we go from this to evaluating new predictions? In other words, given three different weather predictions for tomorrow, which one do I trust?



      The naive approach will be to always pick the one which has the best past average score (maybe a sliding window, taking the past year) and dynamically update the scores after each prediction. But I wonder if there is a more elegant method?



      Also: What are good scoring methods in this example?
      One side condition is that we cannot just average over the predictions, we have to pick one.



      I appreciate any links to relevant literature. I looked into consolidation of data from different sources, but they are trying to solve different problems.










      share|improve this question









      New contributor




      Darina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      Sorry if the title is misleading, I don't know how to describe the issue correctly, which also makes it hard to google.



      Say we have historical weather predictions from three different sources. All predictions are for the same timestamp. We also have the actual weather at that timestamp, so we can assign a score to the quality of each prediction at each timestamp.



      How do we go from this to evaluating new predictions? In other words, given three different weather predictions for tomorrow, which one do I trust?



      The naive approach will be to always pick the one which has the best past average score (maybe a sliding window, taking the past year) and dynamically update the scores after each prediction. But I wonder if there is a more elegant method?



      Also: What are good scoring methods in this example?
      One side condition is that we cannot just average over the predictions, we have to pick one.



      I appreciate any links to relevant literature. I looked into consolidation of data from different sources, but they are trying to solve different problems.







      data data-analysis






      share|improve this question









      New contributor




      Darina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









      New contributor




      Darina is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      share|improve this question




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      edited 3 hours ago







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      asked 3 hours ago









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