Forecasting Workflows

This guide covers how to discover models, train them on your data, and generate forecasts by collaborating with your AI assistant.

Model Discovery

With hundreds of estimators available in sktime, you can ask your assistant to find the perfect model for your task.

Finding the Right Model

Ask your assistant to search for models based on their capabilities or specific tasks.

Example: “Find models that can handle multivariate data” or “Show me probabilistic forecasters for forecasting.”

The assistant will use the list_estimators tool to find matches. Common capabilities you might ask for include:

  • Prediction Intervals: For probabilistic forecasting.

  • Multivariate Support: For data with multiple related time series.

  • Missing Data Handling: For datasets with gaps.

Inspecting a Model

If you want to know more about a specific model, ask for its details.

Example: “Tell me more about the ARIMA model and its parameters.”

The assistant will provide a description and a list of available hyperparameters.

The Fit-Predict Workflow

Once you have a model and data, you can ask the assistant to perform the forecast.

Running a Forecast

You can request a forecast using either a demo dataset or your own loaded data.

Example: “Forecast the next 12 months using ARIMA on the airline dataset.”

The assistant will handle the instantiation, fitting, and prediction steps in one go. If you want to customize the model, you can specify parameters:

Example: “Set up an ARIMA model with order (1, 1, 1) and forecast 6 steps ahead on my loaded data.”

Evaluation

To see how well a model performs, ask your assistant to evaluate it.

Example: “Evaluate this model using 3-fold cross-validation on the airline dataset.”

The assistant will run the evaluation and report back with performance metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Best Practices for Users

  1. Use Natural Language: You don’t need to know the technical tool names. Just tell the assistant what you want to achieve (e.g., “I want to forecast sales for next year”).

  2. Request Reproducibility: After a successful experiment, ask for the code: “Give me the Python code for this workflow.” This allows you to run the same analysis independently later.

  3. Manage Your Session: If you have been working with many different models, you can tell the assistant to “release all handles” or “clear the session” to free up server memory.

  4. Leverage Background Jobs: For very large datasets, tell the assistant to “run this in the background” so you can continue the conversation while the model trains.