cli-automl-forecasting-orange-juice-sales.yml

$schema: https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLJob.schema.json
type: automl

experiment_name: dpv2-cli-automl-forecasting-orange-juice-sales
# name: dpv2-sdk-forecasting-train-job-01
description: A Time-Series Forecasting job using orange juice sales dataset
compute: azureml:cpu-cluster
task: forecasting
primary_metric: normalized_root_mean_squared_error
log_verbosity: info

training_data:
    path: "./training-mltable-folder"
    type: mltable

test_data:
    path: "./test-mltable-folder"
    type: mltable

target_column_name: Quantity
n_cross_validations: auto

featurization:
  mode: custom
  column_name_and_types:
      CPWVOL5: Numeric
  transformer_params:
    imputer:
      - fields: ["Quantity"]
        parameters:
          strategy: constant
          fill_value: 0
      - fields: ["INCOME"]
        parameters:
          strategy: median
      - fields: ["Price"]
        parameters:
          strategy: ffill

limits:
  timeout_minutes: 15
  max_trials: 10
  max_concurrent_trials: 4
  max_cores_per_trial: -1
  trial_timeout_minutes: 15
  enable_early_termination: true

forecasting:
  time_column_name: "WeekStarting"
  time_series_id_column_names: ["Store", "Brand"]
  forecast_horizon: 20
  frequency: "W-THU"

training:
  enable_model_explainability: true
  enable_stack_ensemble: false
  blocked_training_algorithms: []