Merge pull request #31 from Nixtla/v2-issues

hotfix: error raised for short series

R/nixtla_client_forecast.R

@@ -60,20 +60,11 @@ nixtla_client_forecast <- function(df, h=8, freq=NULL, id_col="unique_id", time_
   # Infer frequency if necessary ----
   freq <- infer_frequency(df, freq)
 
-  # Generate fitted values if required ----
-  if(add_history){
-    fitted <- nixtla_client_historic(df=df, freq=freq, id_col=id_col, time_col=time_col, target_col=target_col, level=level, quantiles=quantiles, finetune_steps=finetune_steps, finetune_loss=finetune_loss, clean_ex_first=clean_ex_first)
-  }
-
   # Obtain model parameters ----
   model_params <- .get_model_params(model, freq)
 
-  # Make sure there is enough data ----
-  if(h > model_params$horizon){
-    message("The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.")
-  }
-
-  if(finetune_steps > 0 | !is.null(level)){
+  # Validate input size ----
+  if(finetune_steps > 0 | !is.null(level) | add_history){
     num_rows <- df |>
       dplyr::group_by(.data$unique_id) |>
       dplyr::summarise(initial_size = dplyr::n())
@@ -83,25 +74,21 @@ nixtla_client_forecast <- function(df, h=8, freq=NULL, id_col="unique_id", time_
     }
   }
 
+  # Make sure there is enough data ----
+  if(h > model_params$horizon){
+    message("The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.")
+  }
+
   # Restrict input if necessary ----
   contains_exogenous <- any(!(names(df) %in% c("unique_id", "ds", "y")))
 
   if(!contains_exogenous & finetune_steps == 0 & !add_history){
-    # Input is restricted only when there are no exogenous variables and no finetuning
     if(is.null(level) & is.null(quantiles)){
       input_samples = model_params$input_size
     }else{
       input_samples = 3*model_params$input_size+max(model_params$horizon, h)
     }
 
-    num_rows <- df |>
-      dplyr::group_by(.data$unique_id) |>
-      dplyr::summarise(initial_size = dplyr::n())
-
-    if (any(input_samples > num_rows$initial_size)){
-      stop(paste0("Your time series is too short. Please make sure that each of your series contains at least ", model_params$input_size+model_params$horizon, " observations."))
-    }
-
     df <- df |>
       dplyr::group_by(.data$unique_id) |>
       dplyr::slice_tail(n = input_samples) |>
@@ -267,6 +254,7 @@ nixtla_client_forecast <- function(df, h=8, freq=NULL, id_col="unique_id", time_
 
   # Add fitted values if required ----
   if(add_history){
+    fitted <- nixtla_client_historic(df=df, freq=freq, id_col=id_col, time_col=time_col, target_col=target_col, level=level, quantiles=quantiles, finetune_steps=finetune_steps, finetune_loss=finetune_loss, clean_ex_first=clean_ex_first)
     forecast <- dplyr::bind_rows(fitted, forecast)
   }