sknifedatar
Swiss Knife of Data

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README.md

README.md
In sknifedatar: Swiss Knife of Data

sknifedatar 📦 “Swiss Knife of Data”

Serves primarily as an extension to the modeltime 📦 ecosystem. In addition to some functionalities of spatial data and visualization.

Installation

Not on CRAN yet.

#install.packages("sknifedatar")

Or install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("rafzamb/sknifedatar")

Usage

Multiple models on multiple series functions

libraries

 library(modeltime)
 library(rsample)
 library(parsnip)
 library(recipes)
 library(workflows)
 library(dplyr)
 library(tidyr)
 library(sknifedatar)

Data

 data("emae_series")
 nested_serie = emae_series %>% filter(date < '2020-02-01') %>% nest(nested_column=-sector)

  nested_serie
#> # A tibble: 16 x 2
#>    sector                           nested_column     
#>    <chr>                            <list>            
#>  1 Comercio                         <tibble [193 Ă— 2]>
#>  2 Ensenanza                        <tibble [193 Ă— 2]>
#>  3 Administracion publica           <tibble [193 Ă— 2]>
#>  4 Transporte y comunicaciones      <tibble [193 Ă— 2]>
#>  5 Servicios sociales/Salud         <tibble [193 Ă— 2]>
#>  6 Impuestos netos                  <tibble [193 Ă— 2]>
#>  7 Sector financiero                <tibble [193 Ă— 2]>
#>  8 Mineria                          <tibble [193 Ă— 2]>
#>  9 Agro/Ganaderia/Caza/Silvicultura <tibble [193 Ă— 2]>
#> 10 Electricidad/Gas/Agua            <tibble [193 Ă— 2]>
#> 11 Hoteles/Restaurantes             <tibble [193 Ă— 2]>
#> 12 Inmobiliarias                    <tibble [193 Ă— 2]>
#> 13 Otras actividades                <tibble [193 Ă— 2]>
#> 14 Pesca                            <tibble [193 Ă— 2]>
#> 15 Industria manufacturera          <tibble [193 Ă— 2]>
#> 16 Construccion                     <tibble [193 Ă— 2]>

Recipes

 recipe_1 = recipe(value ~ ., data = emae_series %>% select(-sector)) %>%
  step_date(date, features = c("month", "quarter", "year"), ordinal = TRUE)

Models

 m_auto_arima <- arima_reg() %>% set_engine('auto_arima')

 m_stlm_arima <- seasonal_reg() %>%
   set_engine("stlm_arima")

 m_nnetar <- workflow() %>%
   add_recipe(recipe_1) %>%
   add_model(nnetar_reg() %>% set_engine("nnetar"))

modeltime_multifit

 model_table_emae <- modeltime_multifit(serie = nested_serie %>% head(3),
                                       .prop = 0.8,
                                       m_auto_arima,
                                       m_stlm_arima,
                                       m_nnetar)
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year

 model_table_emae
#> $table_time
#> # A tibble: 3 x 7
#>   sector       nested_column   m_auto_arima m_stlm_arima m_nnetar nested_model  
#>   <chr>        <list>          <list>       <list>       <list>   <list>        
#> 1 Comercio     <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> 2 Ensenanza    <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> 3 Administrac… <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> # … with 1 more variable: calibration <list>
#> 
#> $models_accuracy
#> # A tibble: 9 x 10
#>   name_serie  .model_id .model_desc  .type   mae  mape   mase smape  rmse    rsq
#>   <chr>           <int> <chr>        <chr> <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>
#> 1 Comercio            1 ARIMA(0,1,1… Test   8.54  5.55  0.656  5.69 10.7  0.588 
#> 2 Comercio            2 SEASONAL DE… Test   9.33  6.28  0.717  6.24 11.2  0.415 
#> 3 Comercio            3 NNAR(1,1,10… Test   9.48  6.34  0.728  6.39 11.0  0.465 
#> 4 Ensenanza           1 ARIMA(1,1,1… Test   5.38  3.35  3.90   3.28  6.00 0.730 
#> 5 Ensenanza           2 SEASONAL DE… Test   5.56  3.46  4.03   3.38  6.21 0.726 
#> 6 Ensenanza           3 NNAR(1,1,10… Test   3.10  1.93  2.25   1.91  3.44 0.868 
#> 7 Administra…         1 ARIMA(0,1,1… Test   6.10  3.96 12.6    3.86  7.05 0.0384
#> 8 Administra…         2 SEASONAL DE… Test   6.45  4.19 13.4    4.07  7.61 0.0480
#> 9 Administra…         3 NNAR(1,1,10… Test   6.81  4.43 14.1    4.31  7.47 0.0759

modeltime_multiforecast

forecast_emae <- modeltime_multiforecast(
  model_table_emae$table_time,
  .prop = 0.8
)

forecast_emae %>% 
  select(sector, nested_forecast) %>% 
  unnest(nested_forecast) %>% 
  group_by(sector) %>% 
  plot_modeltime_forecast(
    .legend_max_width = 12,
    .facet_ncol = 2, 
    .line_size = 0.5,
    .interactive = FALSE,
    .facet_scales = 'free_y',
    .title='Forecasting test')

modeltime_multibestmodel

best_model_emae <- modeltime_multibestmodel(
    .table = model_table_emae$table_time,
    .metric = "rmse",
    .minimize = TRUE,
    .forecast = FALSE
  )

best_model_emae
#> # A tibble: 3 x 8
#>   sector       nested_column   m_auto_arima m_stlm_arima m_nnetar nested_model  
#>   <chr>        <list>          <list>       <list>       <list>   <list>        
#> 1 Comercio     <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> 2 Ensenanza    <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> 3 Administrac… <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> # … with 2 more variables: calibration <list>, best_model <list>

modeltime_multirefit

model_refit_emae <- modeltime_multirefit(models_table = best_model_emae)
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year
#> frequency = 12 observations per 1 year

model_refit_emae
#> # A tibble: 3 x 8
#>   sector       nested_column   m_auto_arima m_stlm_arima m_nnetar nested_model  
#>   <chr>        <list>          <list>       <list>       <list>   <list>        
#> 1 Comercio     <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> 2 Ensenanza    <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> 3 Administrac… <tibble [193 ×… <fit[+]>     <fit[+]>     <workfl… <model_time […
#> # … with 2 more variables: calibration <list>, best_model <list>

forecast_emae <- modeltime_multiforecast(
    model_refit_emae,
    .prop = 0.8,
    .h = "1 years"
)

forecast_emae %>% 
  select(sector, nested_forecast) %>% 
  unnest(nested_forecast) %>% 
  group_by(sector) %>% 
  plot_modeltime_forecast(
    .legend_max_width = 12,
    .facet_ncol = 2, 
    .line_size = 0.5,
    .interactive = FALSE,
    .facet_scales = 'free_y',
    .title='Forecasting'
    )

Website

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sknifedatar documentation built on June 1, 2021, 9:08 a.m.

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