Create Model Commandedit

The create-model command will create a new model according to the settings defined in the input file:

loudml create-model avg_temp2.json

The avg_temp2.json file (Or .yml file if YAML format is used) may contain the following settings:

  "bucket_interval": "1m",
  "default_datasource": "my-datasource",
  "features": [
      "default": 0,
      "field": "temp2",
      "measurement": "temperature_series",
      "metric": "avg",
      "name": "avg_temp_feature"
  "interval": 60,
  "max_evals": 10,
  "name": "avg_temp2-model",
  "offset": 30,
  "span": 5,
  "threshold": 30,
  "type": "timeseries"

The supported settings are:


(duration) The bucket aggregation interval


(string) The implicit data source to query and insert data


(array) An array of features derived from the aggregated input data


(duration) The periodic anomaly detection interval


(integer) The integer number of iterations to produce a model with optimal accuracy


(string) Name of this model. This identifier must be unique


(duration) The time offset used when querying the data source


(integer) The span interval, defined as the number of time buckets to use for predicting the next feature values


(integer) The anomaly threshold between 0 and 100


(string) timeseries


(string) Optional. The main timestamp field in your TSDB data source. The default value for this field is timestamp. You can set the value to @timestamp or the value that fits your TSDB mapping.

This above example defines a unique feature, named avg_temp_feature that will be averaged over bucket_interval (1 minute) bucket intervals. The last 5 (span) buckets (5 * 1 minute) will be used for predicting future avg_temp_feature values.

Defining features is straightforward using the unique Loud ML feature description language


The same instructions allow you to define both single variate (unique feature) and multivariate models.

Defining multiple features in the model opens the possiblity to find correlations between features that will help to achieve a better accuracy in the predictions. Say, for example the temperature, and sun exposure measurements both captured in measurements available in the same database.


All features defined in the model will query and aggregate data from the same data source.