MindSphere CLI: mdsp anomaly-detection Command

Syntax:

mdsp anomaly-detection

Help:

mdsp anomaly-detection --help

Alternative form:

mc anomaly-detection

(The CLI was using mc as default command name in older versions)

Description

train anomaly detection models and detect timeseries anomalies *

Usage

Parameter list:

Usage: mc anomaly-detection|ad [options]

train anomaly detection models and detect timeseries anomalies *

Options:
  -m, --mode [template|train|detect]               mode [template | train | detect] (default: "train")
  -o, --on [data|asset]                            on [data | asset] (default: "data")
  -d, --data                                 time series data file (default: "timeseries.mdsp.json")
  -e, --epsilon                           threshold distance
  -s, --clustersize                   minimum cluster size
  -a, --algorithm [EUCLIDEAN|MANHATTAN|CHEBYSHEV]  distance measure algorithm [EUCLIDEAN | MANHATTAN | CHEBYSHEV]
  -n, --modelname                       human-friendly name of the model
  -i, --modelid                           mindsphere model id
  -i, --assetid                           mindsphere asset id
  -n, --aspectname                     mindsphere aspect name
  -f, --from                                 begining of the time range
  -u, --to                                     end of the time range
  -k, --passkey                           passkey
  -y, --retry                              retry attempts before giving up (default: "3")
  -v, --verbose                                    verbose output
  -h, --help                                       display help for command

Examples

Here are some examples of how to use the mdsp anomaly-detection command:


  Examples:

    mc ad --mode template --data timeseries.data.mdsp.json 
                 creates a template for a time series data file
    mc ad --mode train --on data --data timeseries.data.mdsp.json --epsilon 0.5 

                trains a model on the timeserie specified in the data file
    mc ad --mode detect --on data --data timeseries.data.mdsp.json --modelid 
                 detects anomalities of the timeseries in the data file using the model with specified id
    mc ad --mode train --on asset --assetid  --aspectname Environment --epsilon 0.5
                trains a model on the time series of the aspect "Environment" of the asset with the id 
    mc ad --mode detect --on asset --modelid  --assetid  --aspectname Environment --epsilon 0.5
                detect anomalities of the timeseries on the specified asset and aspect with selected model

See MindSphere API documentation for more information about MindSphere APIs.

Further Information

The content of the community tools and libraries documentation pages is licensed under the MIT License.
Siemens API Notice applies.
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