Closed Shafi2016 closed 2 years ago
Hi @Shafi2016 ,
Here is your code corrected. Your problem was in the lag_transformer_grouped()
function. Hope it helps.
library(Quandl)
# Tidymodeling
library(modeltime.ensemble)
library(modeltime)
library(tidymodels)
# Base Models
library(glmnet)
library(xgboost)
# Core Packages
library(tidyverse)
library(lubridate)
library(timetk)
df1 <- Quandl(code = "FRED/PINCOME",
type = "raw",
collapse = "monthly",
order = "asc",
end_date="2017-12-31")
df2 <- Quandl(code = "FRED/GDP",
type = "raw",
collapse = "monthly",
order = "asc",
end_date="2017-12-31")
per <- df1 %>% rename(PI = Value)%>% select(-Date)
gdp <- df2 %>% rename(GDP = Value)
data <- cbind(gdp,per)
data1 <- tk_augment_differences(
.data = data,
.value = GDP:PI,
.lags = 1,
.differences = 1,
.log = TRUE,
.names = "auto") %>%
select(-GDP,-PI) %>%
rename(GDP = GDP_lag1_diff1,PI = PI_lag1_diff1) %>%
drop_na()
horizon <- 15
lag_period <- 15
data_pre_full <- data1 %>%
# Add future window----
bind_rows(
future_frame(.data = .,.date_var = Date, .length_out = horizon)
) %>%
# add lags----
tk_augment_lags(
.value = GDP : PI ,
.lags = lag_period)
# 2.0 STEP 2 - SEPARATE INTO MODELING & FORECAST DATA ----
data_prepared_tbl <- data_pre_full %>%
filter(!is.na(GDP)) %>%
dplyr::select(-GDP : -PI) %>%
drop_na()
forecast_tbl <- data_pre_full %>%
filter(is.na(GDP)) %>%
dplyr::select(-GDP : -PI)
splits <- time_series_split(data_prepared_tbl, assess = 8, cumulative = TRUE)
lag_transformer_grouped <- function(data){
data %>%
tk_augment_lags(GDP_lag15, .lags = 1: 8)
}
model_fit_glmnet <- linear_reg(penalty = 1) %>%
set_engine("glmnet") %>%
fit( GDP_lag15 ~ ., data = training(splits))
model_fit_xgboost <- boost_tree("regression", learn_rate = 0.35) %>%
set_engine("xgboost") %>%
fit( GDP_lag15 ~ ., data = training(splits))
recursive_ensemble_panel <- modeltime_table(
model_fit_glmnet,
model_fit_xgboost
) %>%
ensemble_weighted(loadings = c(4, 6)) %>%
recursive(
transform = lag_transformer_grouped,
train_tail = tail(training(splits), 15),
)
recursive_ensemble_panel
model_tbl <- modeltime_table(
recursive_ensemble_panel
)
model_tbl
# * Accuracy Test ----
#calibration_tbl %>% modeltime_accuracy()
model_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = data_prepared_tbl
) %>%
plot_modeltime_forecast(
.interactive = FALSE,
.conf_interval_show = FALSE,
.facet_ncol = 2
)
If it helps, please close the issue :)
Regards,
Great, Thank You @AlbertoAlmuinha
Hello @mdancho84, @AlbertoAlmuinha, I am using recursive forecasting on data where I have one target variable and a few independent variables. I have given reproducible codes in the end.
With this chunk of codes
I get the following error![image](https://user-images.githubusercontent.com/56795978/141146948-6c07f42c-636b-4f10-a707-90776d965a48.png)