Additive Holt-Winters for anomaly detection #71
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sscdotopen
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Oct 27, 2018
src/main/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWinters.scala
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| nPointsToForecast: Int, | ||
| alpha: Double, | ||
| beta: Double, | ||
| gamma: Double): ModelResults = { |
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In this function, please separate computation of the initial statistics from the invocation of the ListBuffer constructors, the code is really hard to read otherwise
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The initial computation is a bit more readable now, let me know what you think about it.
src/main/scala/com/amazon/deequ/anomalydetection/seasonal/HoltWinters.scala
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added 3 commits
October 27, 2018 09:31
sscdotopen
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Oct 28, 2018
| */ | ||
| private[seasonal] def additiveHoltWinters( | ||
| series: Seq[Double], | ||
| periodicity: Int, |
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do we need a != 0 check for this?
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for now no, maybe later when we want to infer the series cycle length automatically
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added 2 commits
October 28, 2018 18:27
sscdotopen
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Oct 29, 2018
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Description of changes:
Adds additive HW's method for anomaly detection including model selection on the data before the detection interval. The implementation is based on https://otexts.org/fpp2/holt-winters.html. The detector uses the standard deviation of the forecasting residuals to flag anomalies.
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.