NEWS.md
iterate_forecast() can use LLM’s to find the optimal combination of data and inputs to create the most accurate forecast.update_forecast() can take previously trained models from iterate_forecast() to create forecasts on new data fast.ask_agent() can be used to ask questions about the forecast, data, or models to get insights.set_project_info() and set_agent_info to assist in iterating and updating forecasts.get_agent_forecast() to retrieve the final forecast output from an agent run.get_best_agent_run() to retrieve the run metadata information from an agent run.get_summarized_models() to retrieve model summary information from an agent run.get_eda_data() to retrieve the exploratory data analysis results from an agent run.prep_data(). Outliers are removed from the training data, but still kept in the testing splits during time series cross validation.experiment_name within set_run_info() has been changed to project_name to comply with new AI agent capabilities.seasonal_period within prep_models() for more control over multiple seasonal periods in models like tbatstarget_log_transformation within prep_data(), since box_cox has now replaced it for automated power transformationsmultistep_horizon within prep_data()
prep_data() to FALSEarimax, which uses engineered features in addition to any external regressors supplied.list_models(), that lists available models in the packageforecast_time_series(), added new sub components of the finnts forecast process that can be called separately or in a production pipeline. Allows for more control of the forecast process
get_trained_models(), get specific run information thorough get_run_info(), and even retrieve the initial feature engineered data through get_prepped_data().run_model_parallel has been replaced with inner_parallel within forecast_time_series()
forecast_time_series(). Instead please use get_forecast_data() to retrieve Finn forecast outputs.set_run_info(). Please refer to the vignettes for more details.