Anomaly Detection for High-Frequency Data

Hans Meyer 46 Reputation points

Hi there,

Is my understanding correct, that univariate detection is not very suitable when trying to detect anomalies in high-frequency (millisecond-sampling frequency) due to the limited number of datapoints (8640)? Would the alternative just be to use another variable and try multivariate detection with more variables, or is there also a maximal number of datapoints for the inference API?

Thank you in advance!

Azure Anomaly Detector
Azure Anomaly Detector
An Azure cognitive service that is used to embed time-series anomaly detection capabilities into apps to help identify problems quickly.
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  1. Ramr-msft 14,596 Reputation points

    @Hans Meyer Thanks, For uni-variate detection, can you try to use length within 8640 to see how the detection performs. Actually to address the length issue, you can try sample data or aggregate data to a higher frequency. If none of the above works, would like to understand how many data points need to used for one-time detection. Multivariate detection API can not be applied to one variable. You have to prepare multiple signals to detect anomalies. For training, you could send at most 1 million points. For inference, each time you could inference 20,000 points.

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