I am a new user of the packages tidymodels and modeltime. I find these packages really useful for having the same interface of different models from different packages. I try to fine tune a time series with an exponential smoothing from the package forecast::ets. Here is a reproducible example:
library(tidymodels)
library(modeltime)
library(tidyverse)
library(lubridate)
myTS <- c(600, 500, 225, 500, 50, 1025, 1000, 950, 6975, 2400, 2400, 1250, 3500, 3125, 900, 2900, 600, 2150, 900, 1800, 750, 1650, 925, 900, 1075, 1800, 4575, 3725, 1100, 450
, 175, 1275, 1025, 1025, 1575, 1500, 4525, 2000, 1350, 300, 425, 475, 25, 575, 225, 1000, 325, 225, 275, 325, 200, 450, 350, 1175, 250, 400, 650, 2150, 1075, 425)
myDate <- seq(as_date("2016-06-01"), as_date("2021-05-01"), "months")
TabTS <- bind_cols(YearMonth=myDate, Sum_Qty_kg = myTS)
# Split Data 80/20
splits <- initial_time_split(TabTS, prop = 0.8)
data_train <- training(splits)
data_test <- testing(splits)
### Exponential Smoothing Holt-Winters ###
tune_ets <- exp_smoothing(
error = "auto"
, trend = "auto"
, season = "auto"
, smooth_level = tune()
, smooth_trend = tune()
, smooth_seasonal = tune()
) %>%
set_engine("ets")
grid_ets <- grid_regular(
parameters(
list(
smooth_level = smooth_level(range = c(0.1, 0.8))
, smooth_trend = smooth_trend(range = c(0.1, 0.8))
, smooth_seasonal = smooth_seasonal(range = c(0.1, 0.8))
# , error = error()
# , trend = trend(values = c("additive", "multiplicative"))
# , season = season(values = c("additive", "multiplicative"))
)
)
)
# Apply the functions to the inputs and models ----------------------------
ForecastModel_FineTune <- function(tune_model, grid_model, IsBoost = "N"){
### Step 2: Select parameters via Fine tuning ###
# model_recipe <- recipe(Sum_Qty_kg ~ YearMonth , data = data_train)
if(IsBoost == "Y") model_recipe <- recipe(Sum_Qty_kg ~ YearMonth , data = data_train) %>% step_date(YearMonth, features = "month", ordinal = FALSE) %>% step_mutate(date_num = as.numeric(YearMonth)) else model_recipe <- recipe(Sum_Qty_kg ~ YearMonth, data = data_train)
model_workflow <- workflow() %>%
add_model(tune_model) %>%
add_recipe(model_recipe)
resampling_strategy <- data_train %>%
time_series_cv(date_var = YearMonth
, initial = floor(0.8*nrow(data_train))
, assess = nrow(data_train) - floor(0.8*nrow(data_train))
# , skip = "3 months"
# , slice_limit = 4
)
model_tune_results <- model_workflow %>%
tune_grid(resamples = resampling_strategy
, grid = grid_model
, metrics = metric_set(rmse, mae)
)
best_model <- model_tune_results %>%
select_best("rmse")
### Step 3: Finalizing the model ###
model_final <- model_workflow %>%
finalize_workflow(best_model) %>%
fit(data_train) %>%
modeltime_table()
### Step 4: Calibrate the model to a testing set
calibration_model <- model_final %>%
modeltime_calibrate(new_data = data_test)
### Step 5: Testing Set Forecast & Accuracy Evaluation ###
forecast_model <- calibration_model %>%
modeltime_forecast(
new_data = data_test,
actual_data = TabTS
)
return(list(model_tune_results = model_tune_results, forecast_model = forecast_model))
}
forecast_ets <- ForecastModel_FineTune(tune_ets, grid_ets)
When I run this example, I get the following error:
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 4/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 7/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 8/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 12/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 13/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 15/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 16/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 17/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 18/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 20/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 21/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 22/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 23/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 24/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 25/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 26/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 9.5 observations per 1 year
x Slice1: preprocessor 1/1, model 27/27: Error in forecast::ets(outcome, model = model_ets, damped = damping_ets, : No model able to be fitted
frequency = 12 observations per 1 year
Warning message:
In `[.tbl_df`(x, is.finite(x <- as.numeric(x))) :
NAs introduced by coercion
It seems that for some combinations of different parameters values, no model is able to be fitted. How does this problem come from? I tried with another time series and the problem is still there. How can I avoid it?
