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WWW 2018: Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

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也就是说图11,图12,图13 等 ![image](https://user-images.githubusercontent.com/70457740/145753735-2eb29f0d-228d-4f08-a01b-131031a2e7f9.png)

hi, Haowen Xu, i am running donut with g.csv of sample_data, and i use default parameters value, but i got the fscore with 0.04. The result is too bad. But...

如题,看了好久的论文和解读,然后分析了源码也没搞明白,请解答下。

Idk what is this

Hi Haowen Xu, I am trying to run the Donut sample dataset cpu4.csv I have done these following things for invoking cpu4.csv df = pd.read_csv("sample_data/cpu4.csv") timestamp, values, labels = df.timestamp,...

dear author, I'm trying to run Donut with the sample_data "cpu4.csv", and the training losses of 256 epoches are minus which range from -68 to -75. I couldn't find out...

In the paper: 《Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications》 It says that at $4 "We obtain 18 well-maintained business KPIs ... from a large...

It's seems to be a huge number,we train model with n_z = 1 The result will be unstable with a smaller n_z in DonutPredictor?