Sknifedatar

class: center

<img src="images/gif_serie.gif" width="40%" />
# Sknifedatar

**Ajuste y visualización de múltiples modelos**

**sobre múltiples series de tiempo**

2021-06-24

Rafael Zambrano & Karina Bartolomé

---

/* Change the background color to white for shaded rows (even rows) */
.remark-slide thead, .remark-slide tr:nth-child(2n) {
  background-color: white;
  .tfoot .td {background-color: white}
}

.bold-last-item > ul > li:last-of-type,
.bold-last-item > ol > li:last-of-type {font-weight: bold;}
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# ¿Quiénes somos?
.pull-left[
### Rafael Zambrano
- Actuario 🇻🇪
- Data Scientist en Ualá 🚀
- Magister en métodos cuantitativos (en curso)
]
.pull-right[
 
 
<img src="images/imagen_b.jpeg" width="35%" style="display: block; margin: auto;" />
]

.pull-left[
### Karina Bartolomé
- Economista 🇦🇷
- Data Scientist en Ualá 🚀
- Especialista en métodos cuantitativos (en curso)
]
.pull-right[
 
 
<img src="images/imagen_a.jpeg" width="35%" style="display: block; margin: auto;" />
]
---
# ¿Qué es #modeltime?

Es un ecosistema desarrollado por **Matt Dancho** para realizar análisis de series de tiempo mediante un enfoque ordenado (o Tidy) con #tidymodels 📦.

---

# Sknifedatar 📦
#### Una extensión de **#modeltime**

```r
install.packages('sknifedatar')
```
--
<img src="images/sknifedatar.png" width="20%" height="20%" style="display: block; margin: auto;" />
Incluye: 
- **Funciones multifit**: Múltiples modelos en múltiples series de tiempo (sin datos de panel)
- **Workflowsets**: Ajuste de múltiples modelos y recetas de preprocesamiento con modeltime
- **Workflowset multifit**: Ajuste de múltiples modelos y recetas de preprocesamiento sobre múltiples modelos (sin datos de panel)
- **Automagic tabs**: Generación automática de tabs
---
# Agenda para hoy

- **Introducción a modeltime**

- **Multifit**: Ajuste de múltiples modelos en múltiples series de tiempo

- **Workflowsets**: Ajuste de múltiples modelos y recetas de preprocesamiento sobre múltiples modelos

- **Automagic tabs**: Generación automática de tabs en Distill / Rmd

---

# Librerías utilizadas 📚

```r
library(sknifedatar)
library(modeltime)
library(workflowsets)
library(tidymodels)
library(tidyverse)
library(timetk)
library(anomalize) 
```

---

# Datos 📊
**Consumo residencial de gas 4 estados de Estados Unidos, entre 1989 y 2020**

```r
data <- USgas::us_residential %>% rename(value=y) %>% 
 filter(state %in% c('Nevada','Maine',
 'Hawaii','West Virginia')) %>% 
 group_by(state) %>% 
 mutate(value = ifelse(is.na(value),mean(value, na.rm=TRUE),value)) %>% 
 ungroup()
```
--
<table>
 <thead>
 <tr>
 <th style="text-align:left;"> date </th>
 <th style="text-align:left;"> state </th>
 <th style="text-align:right;"> value </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> 1989-01-01 </td>
 <td style="text-align:left;"> Hawaii </td>
 <td style="text-align:right;"> 51 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 1989-02-01 </td>
 <td style="text-align:left;"> Hawaii </td>
 <td style="text-align:right;"> 52 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 1989-03-01 </td>
 <td style="text-align:left;"> Hawaii </td>
 <td style="text-align:right;"> 50 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 1989-04-01 </td>
 <td style="text-align:left;"> Hawaii </td>
 <td style="text-align:right;"> 50 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 1989-05-01 </td>
 <td style="text-align:left;"> Hawaii </td>
 <td style="text-align:right;"> 47 </td>
 </tr>
</tbody>
</table>

---

### Evolución 📈

```r
data %>% group_by(state) %>%
  plot_time_series(date, value) 
```

![](sknifedatar_files/figure-html/unnamed-chunk-17-1.png)

---
### Detección de anomalías 🔍

```r
data %>% group_by(state) %>%
  plot_anomaly_diagnostics(date, value) 
```

![](sknifedatar_files/figure-html/unnamed-chunk-19-1.png)

---
### Descomposición de series de tiempo

.panelset[
.panel[.panel-name[Hawaii]

![](sknifedatar_files/figure-html/unnamed-chunk-20-1.png)
]

.panel[.panel-name[Maine]

![](sknifedatar_files/figure-html/unnamed-chunk-21-1.png)
]

.panel[.panel-name[Nevada]

![](sknifedatar_files/figure-html/unnamed-chunk-22-1.png)
]

.panel[.panel-name[West Virginia]

![](sknifedatar_files/figure-html/unnamed-chunk-23-1.png)
]
]
---
 
 
# Múltiples modelos y una serie 
 
<img src="images/gif_gatos_multi.gif" width="25%" style="display: block; margin: auto;" />

---
## Flujo de trabajo de modeltime

### Preparación de datos ⚙️

* Se selecciona el estado de 🏖 **Hawaii**

```r
data_hawaii <- data %>% 
 filter(state=='Hawaii')
```

--
* Particiona el dataset en train y test ✂️

```r
splits <- data_hawaii %>% 
 initial_time_split(prop = 0.8)
```

---
### Visualización de la partición

```r
splits %>% 
  tk_time_series_cv_plan() %>%
  plot_time_series_cv_plan(date, value) 
```

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---
### Receta 🌮
Se crea una receta de **preprocesamiento**, incluye la fórmula a estimar y un paso adicional que añade variables en función de la fecha.

```r
receta <- recipe(value ~ date, data = training(splits)) %>%
 
 step_timeseries_signature(date) %>% 
 
 step_rm(contains("iso"), 
 contains("minute"), 
 contains("hour"),
 contains("am.pm"), 
 contains("xts"), 
 contains("second"),
 date_index.num, 
 date_wday, 
 date_month)
```

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<td class="gt_row gt_right">51</td>
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<td class="gt_row gt_right">1</td>
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<td class="gt_row gt_right">1</td>
<td class="gt_row gt_right">1</td>
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<td class="gt_row gt_right">32</td>
<td class="gt_row gt_right">32</td>
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<td class="gt_row gt_right">5</td>
<td class="gt_row gt_right">1</td>
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</div>

---
### Modelos 🚀
Definición y ajuste de modelos sobre train

```r
# Modelo: Auto-ARIMA
m_autoarima <- arima_reg() %>% 
 set_engine('auto_arima') %>% 
 fit(value~date, data=training(splits))

# Modelo: exponential smoothing
m_exp_smoothing <- exp_smoothing() %>% 
 set_engine('ets') %>% 
 fit(value~date, data=training(splits))

# Workflow: prophet boosted
m_prophet_boost <- workflow() %>% 
 
 add_recipe(receta) %>% 
 
 add_model(
 prophet_boost(mode='regression') %>%
 set_engine("prophet_xgboost")
 ) %>% 
 
 fit(data = training(splits))
```

---
### Modeltimetable

El objeto central del ecosistema #modeltime 📦 es el **modeltime_table**, el cual incluye todos los modelos entrenados para realizar comparaciones.

```r
modelos <- modeltime_table(m_autoarima,
 m_exp_smoothing,
 m_prophet_boost
 )
```

--
<div data-pagedtable="false">
 <script data-pagedtable-source type="application/json">
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 </script>
</div>

---
### Calibración de modelos 🔧
Se verifica el **rendimiento** de los modelos sobre la partición de test.

```r
calibration_table <- modelos %>% 
 modeltime_calibrate(new_data = testing(splits))
```

--
* Verificación de métricas 🎯

```r
calibration_table %>% 
  modeltime_accuracy()
```

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 </script>
</div>

---
* Forecasting 🔬

Se realiza la **proyección** sobre los datos de testing para luego visualizarla.

```r
forecast_series <- calibration_table %>% 
 modeltime_forecast(
 new_data = testing(splits),
 actual_data = data_hawaii)
```

---

### Verificación visual

```r
forecast_series %>% 
  plot_modeltime_forecast(
    .legend_max_width     = 30, 
    .interactive          = FALSE,
    .conf_interval_alpha  = 0.2
  )
```

![](sknifedatar_files/figure-html/unnamed-chunk-38-1.png)

---
### Selección y reajuste de modelos

Se seleccionan 2 modelos y luego se reajustan ambos modelos en todos los datos (train + test)

```r
refit_tbl <- calibration_table %>%
 filter(.model_id %in% c(1,2)) %>% 
 modeltime_refit(data = data_hawaii)
```

## Proyección 🔮

```r
forecast_final <- refit_tbl %>% 
 modeltime_forecast(
 actual_data = data_hawaii,
 h='2 years'
 )
```

---

### Visualización de la proyección a 2 años

```r
forecast_final %>% 
  plot_modeltime_forecast(
    .legend_max_width = 30, 
    .interactive      = FALSE,
    .conf_interval_alpha = 0.2
)
```

![](sknifedatar_files/figure-html/unnamed-chunk-41-1.png)

---
# Múltiples modelos sobre múltiples series (no panel)

---
## Datos 📊

```r
nest_data <- data %>% nest(nested_column = -state)
```

<div id="htmlwidget-a99c3fa2ebc37f2c182a" class="reactable html-widget" style="width:auto;height:auto;"></div>
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---
## Receta 🌮

```r
receta <- recipe(value ~ date, data = data %>% select(-state)) %>%
 
 step_timeseries_signature(date) %>% 
 
 step_rm(contains("iso"), 
 contains("minute"), 
 contains("hour"),
 contains("am.pm"), 
 contains("xts"), 
 contains("second"),
 date_index.num, 
 date_wday, 
 date_month)
```

---
## Modelos 🚀

Definición de modelos

```r
# Modelo: tbats
m_tbats <-seasonal_reg() %>% 
 set_engine("tbats")

# Modelo stlm_arima
m_seasonal <- seasonal_reg() %>%
 set_engine("stlm_arima")

# Workflow: prophet boosted
m_prophet_boost <- workflow() %>% 
 
 add_recipe(receta) %>% 
 
 add_model(
 prophet_boost(mode='regression') %>%
 set_engine("prophet_xgboost")
 )
```

---
## Modeltime Multifit  ✨

```r
model_table <- modeltime_multifit(serie = nest_data,
 .prop = 0.8,
 m_tbats,
 m_seasonal,
 m_prophet_boost
 ) 
```

---
## Modeltimetable 
* **Table time**

```r
model_table$table_time
```

```r
model_table$table_time$nested_model[[1]]
```

* Verificación de métricas 🎯

```r
model_table$models_accuracy 
```

<div data-pagedtable="false">
 <script data-pagedtable-source type="application/json">
{"columns":[{"label":["name_serie"],"name":[1],"type":["chr"],"align":["left"]},{"label":[".model_id"],"name":[2],"type":["int"],"align":["right"]},{"label":[".model_desc"],"name":[3],"type":["chr"],"align":["left"]},{"label":[".type"],"name":[4],"type":["chr"],"align":["left"]},{"label":["mae"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["mape"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["mase"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["smape"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["rmse"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["rsq"],"name":[10],"type":["dbl"],"align":["right"]}],"data":[{"1":"Hawaii","2":"1","3":"TBATS(1, {0,0}, -, {<12,5>})","4":"Test","5":"2.048344","6":"4.239269","7":"0.5997054","8":"4.213804","9":"2.580558","10":"0.6580835"},{"1":"Hawaii","2":"2","3":"SEASONAL DECOMP: ARIMA(0,1,1)","4":"Test","5":"2.082337","6":"4.306734","7":"0.6096577","8":"4.304328","9":"2.530066","10":"0.6653430"},{"1":"Hawaii","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"3.132737","6":"6.664286","7":"0.9171891","8":"6.379083","9":"3.720455","10":"0.6363483"},{"1":"Maine","2":"1","3":"TBATS(0, {2,2}, -, {<12,5>})","4":"Test","5":"62.628731","6":"20.682037","7":"0.7804519","8":"24.038519","9":"90.084881","10":"0.9317830"},{"1":"Maine","2":"2","3":"SEASONAL DECOMP: ARIMA(4,1,3) WITH DRIFT","4":"Test","5":"75.051502","6":"45.199655","7":"0.9352590","8":"37.520298","9":"94.063015","10":"0.9493728"},{"1":"Maine","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"51.864392","6":"24.763482","7":"0.6463114","8":"24.454316","9":"77.319740","10":"0.8739811"},{"1":"Nevada","2":"1","3":"TBATS(0, {1,2}, 0.888, {<12,4>})","4":"Test","5":"564.120987","6":"13.962006","7":"0.4881914","8":"15.165033","9":"817.142143","10":"0.9324942"},{"1":"Nevada","2":"2","3":"SEASONAL DECOMP: ARIMA(1,1,3) WITH DRIFT","4":"Test","5":"387.735939","6":"11.854816","7":"0.3355474","8":"11.353737","9":"544.458176","10":"0.9518313"},{"1":"Nevada","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"544.514213","6":"12.185886","7":"0.4712236","8":"13.252798","9":"826.694587","10":"0.9370876"},{"1":"West Virginia","2":"1","3":"TBATS(0.348, {4,2}, -, {<12,5>})","4":"Test","5":"338.931416","6":"20.826426","7":"0.4139079","8":"18.116444","9":"511.570278","10":"0.9240290"},{"1":"West Virginia","2":"2","3":"SEASONAL DECOMP: ARIMA(1,1,1)","4":"Test","5":"350.999385","6":"23.373315","7":"0.4286454","8":"20.195734","9":"507.059505","10":"0.9341292"},{"1":"West Virginia","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"393.904332","6":"25.124042","7":"0.4810416","8":"28.680657","9":"546.377757","10":"0.9249577"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[6],"max":[6]},"pages":{}}}
 </script>
</div>

---
## Proyección sobre test 🔬

```r
forecast_series <- modeltime_multiforecast(
 model_table$table_time,
 .prop = 0.9
)
```
---
* **Verificación visual**

```r
forecast_series %>% 
  select(state, nested_forecast) %>% 
  unnest(nested_forecast) %>% 
  group_by(state) %>% 
  plot_modeltime_forecast() 
```

![](sknifedatar_files/figure-html/unnamed-chunk-58-1.png)

---
### Selección de modelos 🥇

Se selecciona el **mejor modelo** para cada serie en función a una métrica.

```r
best_models <- modeltime_multibestmodel(
 .table = forecast_series,
 .metric = "mae"
)
```

---

```r
best_models %>% 
  select(state, nested_forecast) %>% 
  unnest(nested_forecast) %>% 
  group_by(state) %>% 
  plot_modeltime_forecast() 
```

![](sknifedatar_files/figure-html/unnamed-chunk-62-1.png)

---
## Reajuste de los modelos 🌀

* **Se reajustan los modelos para todas las series completas (train + test)**

```r
models_refit <- modeltime_multirefit(best_models)
```

**Proyección** 🔮

* **Se predicen los proximos 2 años para todas las series.**

```r
forecast_final <- models_refit %>% 
 modeltime_multiforecast(.h = "2 years")
```

--
---
**Visualización de la proyección a 2 años**

```r
forecast_final %>% 
  select(state, nested_forecast) %>% 
  unnest(nested_forecast) %>% 
  group_by(state) %>% 
  plot_modeltime_forecast() 
```

![](sknifedatar_files/figure-html/unnamed-chunk-66-1.png)

---

# Workflowsets en múltiples series

---

### Recetas 🌮

```r
# Receta base
recipe_base <- recipe(value~date, data=data_hawaii)

# Características según fecha
recipe_date_extrafeatures <- recipe_base %>% 
 step_date(date, features = c('month','year','quarter','semester'))

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

# Fourier
recipe_date_extrafeatures_fourier <-recipe_date_extrafeatures %>% 
 step_fourier(date, period = 365/12, K = 1)
```

---

### Modelos 🚀

```r
# prophet_xgboost
prophet_boost <- prophet_boost(mode = 'regression') %>% 
 set_engine("prophet_xgboost")

# nnetar
nnetar <- nnetar_reg() %>% 
 set_engine("nnetar")

#auto_arima_xgboost
auto_arima_boost <- arima_boost() %>% 
 set_engine('auto_arima_xgboost')
```

---

### Workflowsets ✨

```r
wfsets <- workflow_set(
 preproc = list(
 base = recipe_base,
 extrafeatures = recipe_date_extrafeatures,
 extrafeatures_lag = recipe_date_extrafeatures_lag,
 extrafeatures_fourier = recipe_date_extrafeatures_fourier
 ),
 models = list(
 M_arima_boost = auto_arima_boost,
 M_prophet_boost = prophet_boost,
 M_nnetar = nnetar
 ),
 cross = TRUE
) 
```

---

👉 El objeto **wfsets** contiene todas las posibles combinaciones de recetas con modelos:

```r
wfsets %>% 
  rmarkdown::paged_table(list(rows.print = 6))
```

<div data-pagedtable="false">
 <script data-pagedtable-source type="application/json">
{"columns":[{"label":["wflow_id"],"name":[1],"type":["chr"],"align":["left"]},{"label":["info"],"name":[2],"type":["list"],"align":["right"]},{"label":["option"],"name":[3],"type":["list"],"align":["right"]},{"label":["result"],"name":[4],"type":["list"],"align":["right"]}],"data":[{"1":"base_M_arima_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"base_M_prophet_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"base_M_nnetar","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_M_arima_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_M_prophet_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_M_nnetar","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_lag_M_arima_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_lag_M_prophet_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_lag_M_nnetar","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_fourier_M_arima_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_fourier_M_prophet_boost","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"},{"1":"extrafeatures_fourier_M_nnetar","2":"<tibble[,4]>","3":"<wrkflw__>","4":"<list [0]>"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[6],"max":[6]},"pages":{}}}
 </script>
</div>

---

### Ajuste de modelos ⚙️

```r
wfs_multifit <- modeltime_wfs_multifit(serie = nest_data,
 .prop = 0.8,
 .wfs = wfsets)
```

<div data-pagedtable="false">
 <script data-pagedtable-source type="application/json">
{"columns":[{"label":["state"],"name":[1],"type":["chr"],"align":["left"]},{"label":["nested_column"],"name":[2],"type":["list"],"align":["right"]},{"label":["base_M_arima_boost"],"name":[3],"type":["list"],"align":["right"]},{"label":["base_M_nnetar"],"name":[4],"type":["list"],"align":["right"]},{"label":["base_M_prophet_boost"],"name":[5],"type":["list"],"align":["right"]},{"label":["extrafeatures_fourier_M_arima_boost"],"name":[6],"type":["list"],"align":["right"]},{"label":["extrafeatures_fourier_M_nnetar"],"name":[7],"type":["list"],"align":["right"]},{"label":["extrafeatures_fourier_M_prophet_boost"],"name":[8],"type":["list"],"align":["right"]},{"label":["extrafeatures_lag_M_arima_boost"],"name":[9],"type":["list"],"align":["right"]},{"label":["extrafeatures_lag_M_nnetar"],"name":[10],"type":["list"],"align":["right"]},{"label":["extrafeatures_lag_M_prophet_boost"],"name":[11],"type":["list"],"align":["right"]},{"label":["extrafeatures_M_arima_boost"],"name":[12],"type":["list"],"align":["right"]},{"label":["extrafeatures_M_nnetar"],"name":[13],"type":["list"],"align":["right"]},{"label":["extrafeatures_M_prophet_boost"],"name":[14],"type":["list"],"align":["right"]},{"label":["nested_model"],"name":[15],"type":["list"],"align":["right"]},{"label":["calibration"],"name":[16],"type":["list"],"align":["right"]}],"data":[{"1":"Hawaii","2":"<tibble[,2]>","3":"<S3: workflow>","4":"<S3: workflow>","5":"<S3: workflow>","6":"<S3: workflow>","7":"<S3: workflow>","8":"<S3: workflow>","9":"<S3: workflow>","10":"<S3: workflow>","11":"<S3: workflow>","12":"<S3: workflow>","13":"<S3: workflow>","14":"<S3: workflow>","15":"<mdl_tm_t[,3]>","16":"<mdl_tm_t[,5]>"},{"1":"Maine","2":"<tibble[,2]>","3":"<S3: workflow>","4":"<S3: workflow>","5":"<S3: workflow>","6":"<S3: workflow>","7":"<S3: workflow>","8":"<S3: workflow>","9":"<S3: workflow>","10":"<S3: workflow>","11":"<S3: workflow>","12":"<S3: workflow>","13":"<S3: workflow>","14":"<S3: workflow>","15":"<mdl_tm_t[,3]>","16":"<mdl_tm_t[,5]>"},{"1":"Nevada","2":"<tibble[,2]>","3":"<S3: workflow>","4":"<S3: workflow>","5":"<S3: workflow>","6":"<S3: workflow>","7":"<S3: workflow>","8":"<S3: workflow>","9":"<S3: workflow>","10":"<S3: workflow>","11":"<S3: workflow>","12":"<S3: workflow>","13":"<S3: workflow>","14":"<S3: workflow>","15":"<mdl_tm_t[,3]>","16":"<mdl_tm_t[,5]>"},{"1":"West Virginia","2":"<tibble[,2]>","3":"<S3: workflow>","4":"<S3: workflow>","5":"<S3: workflow>","6":"<S3: workflow>","7":"<S3: workflow>","8":"<S3: workflow>","9":"<S3: workflow>","10":"<S3: workflow>","11":"<S3: workflow>","12":"<S3: workflow>","13":"<S3: workflow>","14":"<S3: workflow>","15":"<mdl_tm_t[,3]>","16":"<mdl_tm_t[,5]>"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[6],"max":[6]},"pages":{}}}
 </script>
</div>

---
**Performance según rsq de cada modelo en cada serie**
.panelset[
.panel[.panel-name[Hawaii]

![](sknifedatar_files/figure-html/unnamed-chunk-75-1.png)
]

.panel[.panel-name[Maine]

![](sknifedatar_files/figure-html/unnamed-chunk-76-1.png)
]

.panel[.panel-name[Nevada]

![](sknifedatar_files/figure-html/unnamed-chunk-77-1.png)
]

.panel[.panel-name[West Virginia]

![](sknifedatar_files/figure-html/unnamed-chunk-78-1.png)
]
]
---
### Proyecciones 🔬

```r
wfs_multiforecast <- modeltime_wfs_multiforecast(
 wfs_multifit$table_time, .prop=0.8)
```

---
### Selección del mejor modelo 🥇

```r
wfs_bests<- modeltime_wfs_multibestmodel(
 .table = wfs_multiforecast,
 .metric = "rsq",
 .minimize = FALSE
)
```

### Reentrenamiento para todos los datos 🌀

```r
wfs_refit <- modeltime_wfs_multirefit(wfs_bests)
```

---
### Proyecciones a 12 meses 🔮

```r
wfs_forecast <- modeltime_wfs_multiforecast(wfs_refit, 
 .h = "12 month")
```

![](sknifedatar_files/figure-html/unnamed-chunk-85-1.png)
---

# Automagic Tabs 
 
<img src="images/gif_gato_tocadisco.gif" width="50%" style="display: block; margin: auto;" />

---
# ¿Por qué utilizar tabs? 🤔

Mostrar muchos gráficos  📈 o resultados de modelos 🤖 juntos puede generar confusión. Organizar los resultados en solapas permite centrar la atención en ciertos aspectos y no sobrecargar de información.

.panelset[
.panel[.panel-name[👋 Hey!]
Esta es la primera tab

🌟 Hacer click en las tabs para consejos no solicitados 🌟 👆

]

.panel[.panel-name[Consejo 1]
<img src="https://media.tenor.com/images/be8a87467b75e9deaa6cfe8ad0b739a0/tenor.gif" width="50%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Consejo 2]
<img src="https://media.tenor.com/images/6a2cca305dfacae61c5668dd1687ad55/tenor.gif" width="50%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Consejo 3]
<img src="https://media.tenor.com/images/bfde5ad652b71fc9ded82c6ed760355b/tenor.gif" width="50%" style="display: block; margin: auto;" />
]
]
---
## ¿Cómo se crean tabs manualmente?

---

---
## Generación automática de tabs 🙌

👉 **Código inline** , utilizando un dataframe anidado, que incluye una variable del resultado a presentar por tab ('ts_plots'), y una variable agrupadora ('state')

```r
`r automagic_tabs(input_data = nest_plots_1, 
                  panel_name = "state", 
                  .output = "ts_plots")`
```

---

## Contactos ✉

Karina Bartolome [![Twitter Badge](https://img.shields.io/badge/-@karbartolome-1ca0f1?style=flat&labelColor=1ca0f1&logo=twitter&logoColor=white&link=https://twitter.com/karbartolome)](https://twitter.com/karbartolome) [![Linkedin Badge](https://img.shields.io/badge/-karina bartolome-blue?style=flat&logo=Linkedin&logoColor=white&link=https://www.linkedin.com/in/karinabartolome/)](https://www.linkedin.com/in/karinabartolome/) [![Github Badge](https://img.shields.io/badge/-karbartolome-black?style=flat&logo=Github&logoColor=white&link=https://github.com/karbartolome)](https://github.com/karbartolome) [![Website Badge](https://img.shields.io/badge/-Personal%20blog-47CCCC?style=flat&logo=Google-Chrome&logoColor=white&link=https://karbartolome-blog.netlify.app/)](https://karbartolome-blog.netlify.app/)

Rafael Zambrano [![Twitter Badge](https://img.shields.io/badge/-@rafa_zamr-1ca0f1?style=flat&labelColor=1ca0f1&logo=twitter&logoColor=white&link=https://twitter.com/rafa_zamr)](https://twitter.com/rafa_zamr) [![Linkedin Badge](https://img.shields.io/badge/-rafael zambrano-blue?style=flat&logo=Linkedin&logoColor=white&link=https://www.linkedin.com/in/rafael-zambrano/)](https://www.linkedin.com/in/rafael-zambrano/) [![Github Badge](https://img.shields.io/badge/-rafzamb-black?style=flat&logo=Github&logoColor=white&link=https://github.com/rafzamb)](https://github.com/rafzamb) [![Website Badge](https://img.shields.io/badge/-Personal%20blog-47CCCC?style=flat&logo=Google-Chrome&logoColor=white&link=https://rafael-zambrano-blog-ds.netlify.app/)](https://rafael-zambrano-blog-ds.netlify.app/)