It would be nice if automagic_tabs could deploy multiple outputs in the same tabset group. This could be a solution #7 . Also, it avoids calling xaringanExtra before automagic_tabs()

Add a function to create a heatmap plot from a wffits object generated from modeltime_wfs_fit

Functions to adapt modeltime to workflowsets, for individual series.

Hello, I have tried this tutorial

https://github.com/rafzamb/sknifedatar

I found some bug (or restriction maybe?). Sknifedatar will give wrong test dataset if the column date is not ordered by ascending.

Lets try this
ORIGINAL
nested_serie = emae_series %>% filter(date < '2020-02-01') %>% nest(nested_column=-sector)

EXPERIMENT
nested_serie = emae_series %>% filter(date < '2020-02-01') %>% arrange(desc(date)) %>% nest(nested_column=-sector)

The result is like this:

and this

I aware this problem since I tried with another dataset which have random order of date column.

Is there any possibility that I can combine output of multiple forecast into one?

This has reference to the example given in https://karbartolome-blog.netlify.app/posts/workflowsets-timeseries/

Objective is to forecast using additional predictors including date-related predictors. As for example given in above link:

Lagged values: 1 to 6 months lag

recipe_date_extrafeatures_lag <- recipe_date_extrafeatures %>%
step_lag(value, lag = 1:6) %>%
step_ts_impute(all_numeric(), period=365)

The code above has predictors related to Date as well as lags predictors (say X).

FUTURE FORECAST -----

If one needs to forecast 1-year ahead (say, .h = '1 year') after refitting using full data set (training + testing), then one needs also future 1-year X values as well as predictors related to Date. If this the case, it is needed to collect/prepare future 1-year X values as well. Any idea about the concept has been used in the code.

The code given to refit using full data set (training + testing):

wfrefits <- modeltime_wfs_refit(.wfs_results = wffits_best, .serie = data)

Future forecast

wfrefits_forecast <- modeltime_wfs_forecast(.wfs_results = wfrefits,
.serie = data,
.h = '1 year',
.split_prop = split_prop)

Interested to know, how the lags predictors (say X) have been incorporated for 1-year ahead forecast.

Hello, I am new with sknifedatar, so I have tried this tutorial:

https://github.com/rafzamb/sknifedatar

But with different dataset which contains outcome column named "hits" instead "value",

When I tried run this syntax:

model_table_emae = modeltime_multifit(serie = nested_serie %>% head(3),
                                       .prop = 0.8,
                                       m_auto_arima,
                                       m_stlm_arima,
                                       m_nnetar)

Error: Problem with mutate() input m_auto_arima. x object 'value' not found i Input m_auto_arima is purrr::map(...).

This problem solved when I changed colnames "hits" to "value".