Comments on: Detecting Anomalies in Correlated Time Series https://anomaly.io/detect-anomalies-in-correlated-time-series/ Just another WordPress site Thu, 30 May 2019 16:24:00 +0000 hourly 1 http://wordpress.org/?v=4.2.2 By: Bob Zitihttps://anomaly.io/detect-anomalies-in-correlated-time-series/#comment-44 Thu, 21 Dec 2017 02:43:00 +0000 https://anomaly.io/?p=3232#comment-44 This is very interesting, thank you!

Have you ported any of the other blog posts to Python? also, is there any book on anomaly detection you’d personaly recommend?

]]> By: ytushttps://anomaly.io/detect-anomalies-in-correlated-time-series/#comment-42 Thu, 14 Dec 2017 20:30:00 +0000 https://anomaly.io/?p=3232#comment-42 I rewrote this in Python as an exercise and the answer to your question is: you will *not* get the same results.
My implementation is here: https://notebooks.azure.com/anon-te6iza/libraries/anomaly-io-in-python/html/anomaly.io.ipynb see the last cell [35] and the chart bellow with only four anomalies.

]]> By: Bob Zitihttps://anomaly.io/detect-anomalies-in-correlated-time-series/#comment-40 Wed, 15 Nov 2017 00:43:00 +0000 https://anomaly.io/?p=3232#comment-40 Wouldn’t you get the same result if you just looked into the ‘new users’ time serie by itself?
What additional perspective are you getting by looking at the aggregate of both time series?

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