Aident logoAident logo centerAident Loadout
Use CasesPricingDocs
ENStart Now
AppsDatabricks ToolsDatabricks Ml Forecasting Create Experiment
Databricks Tools

Databricks Ml Forecasting Create Experiment

Tool to create a new AutoML forecasting experiment for time series prediction. Use when you need to automatically train and optimize forecasting models on time series data. The experiment will train multiple models and select the best one based on the primary metric.

Write actionRisk level 2 of 5API key connection
Use Databricks Ml Forecasting Create Experiment

Pricing and limits

Aident logo

Product

Aident LoadoutAident Playbook EditorOpen-CUAK

About

AppsSkillsUse CasesDocsBlogPricingDownloadsPrivacy PolicyTerms of Service

Alternatives

vs Composiovs Zapier
llms.txtGet an AI summary of Aident LoadoutSummarize with ChatGPTSummarize with ClaudeSummarize with GeminiSummarize with Perplexity

© Aident Labs, 2024-2026

Pricing
Free
Unified payment
Not currently supported

Inputs

  • datasetstring
    Required
    The name of the dataset table in the format catalog.schema.table or schema.table for use in forecasting
  • horizoninteger
    Required
    The number of time steps into the future to forecast. Must be a positive integer
  • data_dirstring
    DBFS path where intermediate data will be stored during the AutoML run
  • time_colstring
    Required
    The name of the column containing the time series timestamps
  • frequencystring
    Required
    The frequency of the time series data. Common values: 'D' (daily), 'W' (weekly), 'M' (monthly), 'H' (hourly), 'T' or 'min' (minutely)
  • target_colstring
    Required
    The name of the column containing the target values to forecast
  • identity_colarray
    List of column names that identify individual time series in the dataset. Use when the dataset contains multiple time series to forecast independently
  • primary_metricstring
    The primary metric to optimize during model training. Common values: 'smape' (Symmetric Mean Absolute Percentage Error), 'mse' (Mean Squared Error), 'mae' (Mean Absolute Error), 'rmse' (Root Mean Squared Error)
  • experiment_namestring
    Name for the MLflow experiment that will be created to track this AutoML run
  • timeout_minutesinteger
    Maximum time in minutes to run the AutoML forecasting experiment before timing out. If not specified, the experiment will run until completion

Observable output

  • data
    Required
    Data from the action execution
  • errorstring
    Error if any occurred during the execution of the action
  • successfulboolean
    Required
    Whether or not the action execution was successful or not

Skills that use this Action

Related use cases

No verified related use cases are linked yet.

Official source

Provider source

Skills that use this Action

No published Skills explicitly reference this Action yet.