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I would like to create a big list of available time-series datasets for anomaly detection. I'm especially interested in the following:

  • The time-series data should be segmented into cycles
  • Ideally, these cycles should be of the same length
  • These cycles should be labeled as normal/anomalous

But anything goes. I will be sharing the ones I found below.

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  • Kaggle's NAB: a variety of sources such as AWS server metrics, Twitter volume, advertisement clicking metrics, traffic data, and more. Data is labeled.
  • Kaggle's Wafer: manufacturing data, 2K datapoints, 143 labeled anomalies. Measures are taken every 10 milliseconds.
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In addition to Guillermo Mosse answer, I would like to add awesome-TS-anomaly-detection A list of tools & datasets for anomaly detection on time-series data.

A Labeled Anomaly Detection Dataset, version 1.0(16M)

Automatic anomaly detection is critical in today's world where the sheer volume of data makes it impossible to tag outliers manually. The goal of this dataset is to benchmark your anomaly detection algorithm. The dataset consists of real and synthetic time-series with tagged anomaly points. The dataset tests the detection accuracy of various anomaly-types including outliers and change-points. The synthetic dataset consists of time-series with varying trends, noise, and seasonality. The real dataset consists of time-series representing the metrics of various Yahoo services.

The dataset consists of real and synthetic time-series with tagged anomaly points. The dataset tests the detection accuracy of various anomaly-types including outliers and change-points.

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