RegressionModelForecastPlotLevels

Assesses the alignment between forecasted and observed values in regression models through visual plots, including handling data transformations.

Purpose

The RegressionModelForecastPlotLevels test aims to visually assess the performance of a series of regression models by comparing their forecasted values against the actual observed values in both training and test datasets. This test helps determine the accuracy of the models and can handle specific data transformations before making the comparison, providing a comprehensive evaluation of model performance.

Test Mechanism

The test mechanism involves initializing the RegressionModelForecastPlotLevels class with an optional transformation parameter. The class then:

  • Checks for the presence of model objects and raises a ValueError if none are found.
  • Processes each model to generate predictive forecasts for both training and testing datasets.
  • Contrasts these forecasts with the actual observed values.
  • Produces plots to visually compare forecasted and observed values for both raw and transformed datasets.
  • Handles specified transformations (e.g., “integrate”) by performing cumulative sums to create a new series before plotting.

Signs of High Risk

  • Significant deviation between forecasted and observed values in training or testing datasets.
  • Patterns suggesting overfitting or underfitting.
  • Large discrepancies in the plotted forecasts, indicating potential issues with model generalizability and precision.

Strengths

  • Visual Evaluations: Provides an intuitive, visual way to assess multiple regression models, aiding in easier interpretation and evaluation of forecast accuracy.
  • Transformation Handling: Can process specified data transformations such as “integrate,” enhancing flexibility.
  • Detailed Perspective: Assesses performance on both training and testing datasets, offering a comprehensive view of model behavior.

Limitations

  • Subjectivity: Relies heavily on visual interpretation, which may vary between individuals.
  • Limited Transformation Capability: Supports only the “integrate” transformation; other complex transformations might not be handled.
  • Overhead: Plotting can be computationally intensive for large datasets, increasing runtime.
  • Numerical Measurement: Does not provide a numerical metric to quantify forecast accuracy, relying solely on visual assessment.