# Create a Recursive Time Series Model from a Parsnip or Workflow Regression Model ```r recursive(object, transform, train_tail, id = NULL, chunk_size = 1, ...) ``` ## Arguments - `object`: An object of `model_fit` or a fitted `workflow` class - `transform`: A transformation performed on `new_data` after each step of recursive algorithm. * Transformation Function: Must have one argument `data` (see examples) - `train_tail`: A tibble with tail of training data set. In most cases it'll be required to create some variables based on dependent variable. - `id`: (Optional) An identifier that can be provided to perform a panel forecast. A single quoted column name (e.g. `id = "id"`). - `chunk_size`: The size of the smallest lag used in `transform`. If the smallest lag necessary is n, the forecasts can be computed in chunks of n, which can dramatically improve performance. Defaults to 1. Non-integers are coerced to integer, e.g. `chunk_size = 3.5` will be coerced to integer via `as.integer()`. - `...`: Not currently used. ## Returns An object with added `recursive` class ## Details What is a Recursive Model? A **recursive model** uses predictions to generate new values for independent features. These features are typically lags used in autoregressive models. It's important to understand that a recursive model is only needed when the Lag Size \< Forecast Horizon. Why is Recursive needed for Autoregressive Models with Lag Size \% group_by(id) %>% future_frame( .length_out = FORECAST_HORIZON, .bind_data = TRUE ) %>% ungroup() # TRANSFORM FUNCTION ---- # - Function runs recursively that updates the forecasted dataset lag_roll_transformer <- function(data){ data %>% # Lags tk_augment_lags(value, .lags = 1:12) %>% # Rolling Features mutate(rolling_mean_12 = lag(slide_dbl( value, .f = mean, .before = 12, .complete = FALSE ), 1)) } # Data Preparation m750_rolling <- m750_extended %>% lag_roll_transformer() %>% select(-id) train_data <- m750_rolling %>% drop_na() future_data <- m750_rolling %>% filter(is.na(value)) # Modeling # Straight-Line Forecast model_fit_lm <- linear_reg() %>% set_engine("lm") %>% # Use only date feature as regressor fit(value ~ date, data = train_data) # Autoregressive Forecast model_fit_lm_recursive <- linear_reg() %>% set_engine("lm") %>% # Use date plus all lagged features fit(value ~ ., data = train_data) %>% # Add recursive() w/ transformer and train_tail recursive( transform = lag_roll_transformer, train_tail = tail(train_data, FORECAST_HORIZON) ) model_fit_lm_recursive # Forecasting modeltime_table( model_fit_lm, model_fit_lm_recursive ) %>% update_model_description(2, "LM - Lag Roll") %>% modeltime_forecast( new_data = future_data, actual_data = m750 ) %>% plot_modeltime_forecast( .interactive = FALSE, .conf_interval_show = FALSE ) # MULTIPLE TIME SERIES (PANEL DATA) ----- m4_monthly FORECAST_HORIZON <- 24 m4_extended <- m4_monthly %>% group_by(id) %>% future_frame( .length_out = FORECAST_HORIZON, .bind_data = TRUE ) %>% ungroup() # TRANSFORM FUNCTION ---- # - NOTE - We create lags by group lag_transformer_grouped <- function(data){ data %>% group_by(id) %>% tk_augment_lags(value, .lags = 1:FORECAST_HORIZON) %>% ungroup() } m4_lags <- m4_extended %>% lag_transformer_grouped() train_data <- m4_lags %>% drop_na() future_data <- m4_lags %>% filter(is.na(value)) # Modeling Autoregressive Panel Data model_fit_lm_recursive <- linear_reg() %>% set_engine("lm") %>% fit(value ~ ., data = train_data) %>% recursive( id = "id", # We add an id = "id" to specify the groups transform = lag_transformer_grouped, # We use panel_tail() to grab tail by groups train_tail = panel_tail(train_data, id, FORECAST_HORIZON) ) modeltime_table( model_fit_lm_recursive ) %>% modeltime_forecast( new_data = future_data, actual_data = m4_monthly, keep_data = TRUE ) %>% group_by(id) %>% plot_modeltime_forecast( .interactive = FALSE, .conf_interval_show = FALSE ) ``` ## See Also * `panel_tail()` - Used to generate tails for multiple time series groups.

recursive function