TimeSeriesR2SquareBySegments

Plot R-Squared values for each model over specified time segments and generate a bar chart with the results.

Purpose: The purpose of this function is to plot the R-Squared values for different models applied to various segments of the time series data.

Parameters: - datasets: List of datasets to evaluate. - models: List of models to evaluate. - segments: Dictionary with ‘start_date’ and ‘end_date’ keys containing lists of start and end dates for each segments. If None, the time series will be segmented into two halves.

Test Mechanism: The function iterates through each dataset-model pair, calculates the R-Squared values for specified time segments, and generates a bar chart with these results.

Signs of High Risk: - If the R-Squared values are significantly low for certain segments, it could indicate that the model is not explaining much of the variability in the dataset for those segments.

Strengths: - Provides a visual representation of model performance across different time segments. - Allows for identification of segments where models perform poorly.

Limitations: - Assumes that the dataset is provided as a DataFrameDataset object with y, y_pred, and feature_columns attributes. - Requires that dataset.y_pred(model) returns the predicted values for the model. - Assumes that y_true and y_pred are pandas Series with datetime indices.