New AI Agent Capabilities
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.New Chronos2 Model Integration
jsonlite and httrchronos-bolt-base foundation model. Uses the same Chronos API as chronos2 but does not support external regressors. Passes model_type = "chronos-bolt-base" to the API.chronos-bolt-tiny foundation model. Lightweight Chronos model variant using the same API as chronos2 and chronos-bolt-base. Does not support external regressors. Passes model_type = "chronos-bolt-tiny" to the API.New TimesFM Integration
TIMESFM_API_URL and TIMESFM_API_TOKEN environment variablesNew TimeGPT Integration
Updated Train Model function
Updated how outliers are handled in prep_data(). Outliers are removed from the training data, but still kept in the testing splits during time series cross validation.
subscript out of bounds failures for time series combos containing non-ASCII characters. File name hashes are now stable regardless of how the text was read in (e.g. read.csv vs vroom), so input data, EDA, and forecast outputs resolve to the same file.null_converter() crash in agent workflow when input is NA.experiment_name within set_run_info() has been changed to project_name to comply with new AI agent capabilities.qs package to qs2 for fast object serialization. The qs2 package is actively maintained and CRAN-ready with improved compression. Files previously saved with qs format cannot be read by qs2; any cached .qs files from prior runs will need to be regenerated.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
prep_data()prep_models()train_models()ensemble_models()final_models()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.