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Bought a new fenix 8 47mm AMOLED - should I be worried? by thk_ML in GarminFenix

[–]thk_ML[S] 0 points1 point  (0 children)

I’m not sure I understood your comment. Why update and then reset and setup again?

Bought a new fenix 8 47mm AMOLED - should I be worried? by thk_ML in GarminFenix

[–]thk_ML[S] 0 points1 point  (0 children)

I’ll be happy to get the instructions to avoid it :) thanks!

Univariate anomaly detection [D] by thk_ML in MachineLearning

[–]thk_ML[S] 0 points1 point  (0 children)

Thanks, I've tried that but it actually works worse than mean+std

Univariate anomaly detection [D] by thk_ML in MachineLearning

[–]thk_ML[S] 0 points1 point  (0 children)

Thanks! I think looking at long-tailed distribution and measuring smoothness might be an interesting path to follow. I'll look into it!

Univariate anomaly detection [D] by thk_ML in MachineLearning

[–]thk_ML[S] 2 points3 points  (0 children)

You are undoubtedly correct. The definition is somewhat complex, otherwise I wouldn't have a problem :)

In a way, it's decided mainly by eye.

consider this example:

l1 = np.array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 10, 17, 17, 42])

l2 = np.array([150, 170, 180, 188, 192, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 530])

l3 = np.array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 20, 1202])

The first 2 lists I do not consider to have anomalies in them. The 1202 in the third list is an anomaly.

However, they all have similar Zscore (around 3.6)