Comments on: Detecting Anomalies with Moving Median Decomposition https://anomaly.io/anomaly-detection-moving-median-decomposition/ Just another WordPress site Thu, 30 May 2019 16:24:00 +0000 hourly 1 http://wordpress.org/?v=4.2.2 By: Navidenhttps://anomaly.io/anomaly-detection-moving-median-decomposition/#comment-45 Fri, 29 Dec 2017 10:05:00 +0000 https://anomaly.io/?p=3090#comment-45 “7” is the seasonality period –> weekly data –> 7 days
after using runmed(x,7) we remove the outlier effect from trend so the random part will have the outlier effect –> raw data(has outlier) / trend(no outlier) / seasonality(no outlier) = random(with outlier effect)
so we need to use runmed() again to remove it. I don’t know why exactly the author used 3 periods but I think it wont change the results too much.
the only thing to note is that the above “raw data(has outlier) / trend(no outlier) / seasonality(no outlier) = random(with outlier effect)” doesn’t accurately represent the method showed in this article because the author has used colMean() for calculating the seasonality which results in having a dirty seasonality (containing outlier effect) while I think “colMedians()” should be used instead (robustbase library)

]]> By: Demetrios Eliadeshttps://anomaly.io/anomaly-detection-moving-median-decomposition/#comment-38 Mon, 04 Sep 2017 13:06:00 +0000 https://anomaly.io/?p=3090#comment-38 Hi Martin, why didn’t you use the moving median filter for the random variable in the first example, in the same way you did in the second? The results appear to be similar when applied.

]]> By: Bob Doehttps://anomaly.io/anomaly-detection-moving-median-decomposition/#comment-30 Tue, 18 Jul 2017 00:51:00 +0000 https://anomaly.io/?p=3090#comment-30 hi I had a question about this line:

rm_random = runmed(random[!is.na(random)], 3)

Why are we setting the window to 3 in this instance? Is it just arbitrary?

]]> By: Sana Munawarhttps://anomaly.io/anomaly-detection-moving-median-decomposition/#comment-28 Fri, 19 May 2017 14:09:00 +0000 https://anomaly.io/?p=3090#comment-28 Thank you for this amazing article and other articles on this topic.

]]> By: Sana Munawarhttps://anomaly.io/anomaly-detection-moving-median-decomposition/#comment-27 Fri, 19 May 2017 14:08:00 +0000 https://anomaly.io/?p=3090#comment-27 m = t(matrix(data = detrend, nrow = 7))
rm_random = runmed(random[!is.na(random)], 3)

I am not sure what do numbers 7 and 3 mean in the above lines. My data is with frequence 23 observations per year. I have total 395 observations. Sorry for my ignorance but I dont understand what values should I use instead.

]]> By: Aleksandras Urbonashttps://anomaly.io/anomaly-detection-moving-median-decomposition/#comment-16 Thu, 15 Dec 2016 20:54:00 +0000 https://anomaly.io/?p=3090#comment-16 Well done, I like it.

Could you fix the code: anomalyL[!is.na(anomalyL$value)], because all columns are being selected, and a comma is missing. right before the closing bracket: anomalyL[!is.na(anomalyL$value) , ]

Have a lovely day,

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