Sknifedatar

# {Sknifedatar}  📦

**Un paquete para el modelado y visualización de múltiples series de tiempo**

Rafael Zambrano & Karina Bartolomé

Octubre 2022

---

# ¿Quiénes somos?

.pull-left[
### Rafael Zambrano
- **Actuary / Data Scientist**
]
.pull-right[
 
 
<img src="data:image/png;base64,#images/imagen_b.jpeg" width="35%" style="display: block; margin: auto;" />
]

.pull-left[
### Karina Bartolomé
- **Economist / Data Scientist**
]
.pull-right[
 
 
<img src="data:image/png;base64,#images/imagen_a.jpeg" width="35%" style="display: block; margin: auto;" />
]
---

## Ecosistema {tidymodels-modeltime}

{modeltime} fue desarrollado por **Matt Dancho** para realizar análisis de series de tiempo mediante un enfoque ordenado (o Tidy) con {tidymodels} 📦.

---

# {sknifedatar} 📦

Cómo surge sknifedatar?

#### Una extensión de **{modeltime}**

```r
install.packages('sknifedatar')
```

---

# {sknifedatar} 📦

### Funcionalidades

- **Ajustes múltiples**: {Modeltime} para múltiples series de tiempo (sin datos de panel)
- **Adaptación de workflowsets al modelado de series de tiempo**: Múltiples modelos y recetas de preprocesamiento con {tidymodels}
- **Automagic tabs**: Generación automática de tabs para presentaciòn de resultados (rmarkdown)

---

# Objetivo de la Charla

Presentar el paquete **{skinifedatar}** 📦 para el modelado y visualización de series de tiempo, tomando como caso de uso datos de viajes en transporte público, Argentina (2021-2022).

Se muestra la compatibilidad con diversos componentes del ecosistema **{tidymodels}** como **{modeltime}**  y **{workflosets}**.

---

# Agenda

#### ✅ Caso de uso
#### ✅ Múltiples modelos en una serie
#### ✅ Múltiples modelos en múltiples series
#### ✅ Bonus track

---
class: chapter-slide

# Caso de Uso

---

# Caso de Uso

<img src="data:image/png;base64,#https://upload.wikimedia.org/wikipedia/commons/thumb/1/14/SUBE_frente.svg/413px-SUBE_frente.svg.png" width="35%" style="display: block; margin: auto;" />
]

- El **Sistema Único de Boleto Electrónico (SUBE)** es una tarjeta para abonar medios de transporte público en Argentina.

- Los viajes realizados quedan registrados en un **portal de datos abiertos**.

- Se busca **proyectar** cómo será el uso de diversas líneas en los próximos días o meses.

]

---

# Caso de Uso

### Datos 📊

Se utilizan datos de [SUBE - Cantidad de transacciones (usos) por fecha
](https://datos.gob.ar/dataset/transporte-sube---cantidad-transacciones-usos-por-fecha).

```r
data <- read_csv('data/df_sube.csv') %>% 
 rename(date=fecha, value=n)
```
--
<table>
 <thead>
 <tr>
 <th style="text-align:left;"> date </th>
 <th style="text-align:left;"> linea </th>
 <th style="text-align:right;"> value </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> 2021-01-01 </td>
 <td style="text-align:left;"> BSAS_LINEA_203 </td>
 <td style="text-align:right;"> 20189 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 2021-01-01 </td>
 <td style="text-align:left;"> BSAS_LINEA_501G </td>
 <td style="text-align:right;"> 25760 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 2021-01-01 </td>
 <td style="text-align:left;"> FFCC ROCA </td>
 <td style="text-align:right;"> 25576 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 2021-01-01 </td>
 <td style="text-align:left;"> FFCC SAR </td>
 <td style="text-align:right;"> 4049 </td>
 </tr>
</tbody>
</table>

---

# Caso de Uso

* **Evolución de las series 📈**

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

![](data:image/png;base64,#slides_files/figure-html/unnamed-chunk-16-1.png)

---

# Múltiples modelos sobre una serie

<div: Introducción a modeltime

---

# Múltiples modelos sobre una serie

* **Seleccion de datos**: Se selecciona el departamento **Ferrocarril Roca**

```r
data_ffcc_roca <- data %>% 
 filter(linea=='FFCC ROCA') %>% 
 select(-linea) %>% ungroup()
```

```r
data_ffcc_roca %>% head(5) %>% kableExtra::kable(format = "html")
```

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> date </th>
 <th style="text-align:right;"> value </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> 2021-01-01 </td>
 <td style="text-align:right;"> 25576 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 2021-01-02 </td>
 <td style="text-align:right;"> 112401 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 2021-01-03 </td>
 <td style="text-align:right;"> 58265 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 2021-01-04 </td>
 <td style="text-align:right;"> 231781 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> 2021-01-05 </td>
 <td style="text-align:right;"> 233965 </td>
 </tr>
</tbody>
</table>

---

## Múltiples modelos sobre una serie

* **Partición de datos**: Se particiona el dataset en train y test ✂️

```r
splits <- data_ffcc_roca %>% initial_time_split(prop = 0.9)
```

```r
splits %>% tk_time_series_cv_plan() %>%
  plot_time_series_cv_plan(date, value, .title='Partición temporal') 
```

<div id="htmlwidget-3092a13275dd9a60046a" style="width:648px;height:360px;" class="plotly html-widget"></div>
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---

## Múltiples modelos sobre una serie

* **Preprocesamiento / Recetas 🧁**: 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_date(date, 
 features = c('week', 'month','year','quarter','semester'))
```

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<table class="gt_table">
 
 <thead class="gt_col_headings">
 <tr>
 <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col">date</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col">value</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col">date_week</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col">date_month</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col">date_year</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col">date_quarter</th>
 <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col">date_semester</th>
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 <tbody class="gt_table_body">
 <tr><td class="gt_row gt_right">2021-01-01</td>
<td class="gt_row gt_right">25576</td>
<td class="gt_row gt_right">1</td>
<td class="gt_row gt_center">Jan</td>
<td class="gt_row gt_right">2021</td>
<td class="gt_row gt_right">1</td>
<td class="gt_row gt_right">1</td></tr>
 </tbody>
 
 
</table>
</div>

---

## Múltiples modelos sobre una serie

* **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))
```

```r
# Modelo: regresión lineal
m_reg_lineal <- linear_reg() %>%
 set_engine("lm") %>%
 fit(value ~ as.numeric(date) + 
 factor(month(date, label = TRUE), ordered = FALSE)+
 factor(wday(date, label=TRUE), ordered=FALSE),
 data = training(splits))
```

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

---

## Múltiples modelos sobre una serie

* **Modeltime Table**

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_reg_lineal,
 m_prophet_boost
 )
```

---

## Múltiples modelos sobre una serie

* **Métricas en Test**

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

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

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</div>

---

## Múltiples modelos sobre una serie

* **Métricas en Test**

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

---

## Múltiples modelos sobre una serie

* **Métricas en Test**

```r
forecast_series %>% plot_modeltime_forecast()
```

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---

## Múltiples modelos sobre una serie

* **Refit y pronostico (próximos 2 meses)**

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

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

---
## Múltiples modelos sobre una serie

* **Visualización de la proyección a 2 meses**

```r
forecast_final %>% plot_modeltime_forecast()
```

![](data:image/png;base64,#slides_files/figure-html/unnamed-chunk-38-1.png)

---

# Múltiples modelos sobre múltiples series

<div:Introducción a sknifedatar

---

## Múltiples modelos - múltiples series

* **Selección de datos**

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

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---

## Múltiples modelos - múltiples series

* **Modeltime Table Multifit**

```r
receta <- recipe(value ~ date, data = data %>% select(-linea)) %>%
 
 step_date(
 date,
 features = c('week', 'month', 'year', 'quarter', 'semester'))
```

---

## Múltiples modelos - múltiples series

* **Definición de modelos**

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

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

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

---

## Múltiples modelos - múltiples series

* **Modeltime Table Multifit**

```r
model_table <- modeltime_multifit(serie = nest_data,
 .prop = 0.9,
 m_tbats,
 m_seasonal,
 m_prophet_boost
 ) 
```

---

## Múltiples modelos - múltiples series

* **Métricas en la partición de evaluación**

```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":"BSAS_LINEA_203","2":"1","3":"TBATS(1, {0,0}, -, {<7,3>})","4":"Test","5":"8877","6":"10.0","7":"0.53","8":"9.0","9":"12399","10":"0.73"},{"1":"BSAS_LINEA_203","2":"2","3":"SEASONAL DECOMP: ARIMA(2,1,1)","4":"Test","5":"8897","6":"9.4","7":"0.54","8":"8.5","9":"12450","10":"0.73"},{"1":"BSAS_LINEA_203","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"16928","6":"18.2","7":"1.02","8":"15.9","9":"19774","10":"0.65"},{"1":"BSAS_LINEA_501G","2":"1","3":"TBATS(1, {0,0}, -, {<7,2>})","4":"Test","5":"14809","6":"15.7","7":"0.52","8":"12.9","9":"21342","10":"0.68"},{"1":"BSAS_LINEA_501G","2":"2","3":"SEASONAL DECOMP: ARIMA(1,1,2)","4":"Test","5":"12353","6":"11.8","7":"0.43","8":"9.7","9":"19708","10":"0.73"},{"1":"BSAS_LINEA_501G","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"22197","6":"21.7","7":"0.78","8":"17.5","9":"28177","10":"0.69"},{"1":"FFCC ROCA","2":"1","3":"TBATS(1, {2,0}, -, {<7,3>})","4":"Test","5":"49290","6":"18685.4","7":"0.39","8":"19.7","9":"83561","10":"0.74"},{"1":"FFCC ROCA","2":"2","3":"SEASONAL DECOMP: ARIMA(1,1,1)","4":"Test","5":"42667","6":"18311.3","7":"0.34","8":"13.8","9":"77579","10":"0.73"},{"1":"FFCC ROCA","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"56434","6":"19147.2","7":"0.45","8":"22.1","9":"93702","10":"0.75"},{"1":"FFCC SAR","2":"1","3":"TBATS(0.26, {0,0}, -, {<7,3>})","4":"Test","5":"23446","6":"22340.4","7":"0.28","8":"16.4","9":"48855","10":"0.71"},{"1":"FFCC SAR","2":"2","3":"SEASONAL DECOMP: ARIMA(3,1,3)","4":"Test","5":"24818","6":"22542.7","7":"0.30","8":"17.2","9":"49441","10":"0.71"},{"1":"FFCC SAR","2":"3","3":"PROPHET W/ XGBOOST ERRORS","4":"Test","5":"37468","6":"23751.8","7":"0.45","8":"30.9","9":"61321","10":"0.69"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[6],"max":[6]},"pages":{}}}
 </script>
</div>

---

## Múltiples modelos - múltiples series

* **Forecast sobre la partición de evaluación**

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

---

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

![](data:image/png;base64,#slides_files/figure-html/unnamed-chunk-51-1.png)

---

## Múltiples modelos - múltiples series

* **Selección del mejor modelo para cada serie**

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

---

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

![](data:image/png;base64,#slides_files/figure-html/unnamed-chunk-54-1.png)

---

## Múltiples modelos - múltiples series

* **Refit y pronostico (próximos 2 meses)**

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

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

```r
forecast_final %>% 
  select(linea, nested_forecast) %>% 
  unnest(nested_forecast) %>% 
  group_by(linea) %>% 
  plot_modeltime_forecast(.interactive=FALSE)
```

---

## Múltiples modelos - múltiples series

* **Refit y pronostico (próximos 2 meses)**

![](data:image/png;base64,#slides_files/figure-html/unnamed-chunk-58-1.png)

---

# Bonus track: workflowsets
---

## {Worflowsets} sobre series de tiempo

---

# Bonus track: automagic tabs

---
# ¿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.

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

]

.panel[.panel-name[Consejo 1]
<img src="data:image/png;base64,#https://media.tenor.com/images/be8a87467b75e9deaa6cfe8ad0b739a0/tenor.gif" width="50%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Consejo 2]
<img src="data:image/png;base64,#https://media.tenor.com/images/6a2cca305dfacae61c5668dd1687ad55/tenor.gif" width="50%" style="display: block; margin: auto;" />
]

.panel[.panel-name[Consejo 3]
<img src="data:image/png;base64,#https://media.tenor.com/images/bfde5ad652b71fc9ded82c6ed760355b/tenor.gif" width="50%" style="display: block; margin: auto;" />
]
]
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## ¿Cómo se crean tabs manualmente?

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## Generación automática de tabs 🙌

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

![](data:image/png;base64,#images/automagic_tabs_gif.gif)

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## Contactos ✉

Karina Bartolome [![Twitter Badge](data:image/png;base64,#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](data:image/png;base64,#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](data:image/png;base64,#https://img.shields.io/badge/-karbartolome-black?style=flat&logo=Github&logoColor=white&link=https://github.com/karbartolome)](https://github.com/karbartolome) [![Website Badge](data:image/png;base64,#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](data:image/png;base64,#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](data:image/png;base64,#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](data:image/png;base64,#https://img.shields.io/badge/-rafzamb-black?style=flat&logo=Github&logoColor=white&link=https://github.com/rafzamb)](https://github.com/rafzamb) [![Website Badge](data:image/png;base64,#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/)

## {Sknifedatar}  📦

[Gitpage](https://rafzamb.github.io/sknifedatar/)

[Workflowsets sobre múltiples series](https://rafzamb.github.io/sknifedatar/articles/workflowsets_multi_times.html)

[Automagic tabs](https://rafzamb.github.io/sknifedatar/articles/automatic_tabs.html)

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# Muchas Gracias!!!