Test Set Sample Uncertainty in PostProcessing
Test Set Sample Uncertainty via bootstrap method
To get a better understanding of the trained model performance, sample unvertainty using a bootstrap method is performed on test data.
Target
- Get estimate about sample uncertainty of trained model
- Get also estimate about sample uncertainty of competitors
- Visualisation of estimates
Working steps of method
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set number of permutations (default=1000)
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set block length of permutation blocks (default=monthly)
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set whether forecasts are harmonised before error calculation or not (default=true) -
divide data into blocks: along time axis, all stations together
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get total block count
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calculate error metric for each block (average for each block on all stations and ahead steps)
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draw random block with replacement
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draw n="total block count" times
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calculate average error metric for single permutation
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repeat n times and collect error of each permutation
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calculate overall statistical metrics (percentiles, ...)
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store overall statistical metrics on disk
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visualise overall statistical metrics with box-and-whiskers plot
Design
- error metric for each block:
time (block date, e.g. "2016-10") x error value
- average error metric for single permutation:
number of permutation x error value