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 trend, noise and seasonality. The real dataset consists of time-series representing the metrics of various Yahoo services.

Here are all the papers published on this Webscope Dataset:

• Design of a Large-Scale Anomaly Detection Algorithm for Time Series in the context of Wireless Network Operations
• A simple method for unsupervised anomaly detection: An application to Web time series data

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