Effects of External Conditions on Air Quality
Prepare Plot Data and Plot Counterfactuals
Descriptive plot of daily time series data
Run the dynamic regression model
Train a Feedforward Neural Network (FNN) in a Counterfactual Scenario.
Run gradient boosting model with lightgbm
Run random forest model with ranger
Calculates performance metrics of a business-as-usual model
Calculates summary statistics for predictions and true values
Clean and Optionally Aggregate Environmental Data
Copy Default Parameters File
Removes trend from data
Estimates size of the external effect
Get Available Meteorological Components
Load Parameters from YAML File
Load UBA Data from Directory
Prepare Data for Training a model
Rescale predictions to original scale.
Restors the trend in the prediction
Full counterfactual simulation run
Standardize Training and Application Data
Split Data into Training and Application Datasets
Analyzes the impact of external conditions on air quality using counterfactual approaches, featuring methods for data preparation, modeling, and visualization.