How to Run a Synthetic Forecast

The Dataset

The first thing that a forecast needs a data to be forecasted. The SynthCast provides a example of how it expected a dataset to look like, the code bellow loads the package and the example dataset:

library(knitr)
library(SynthCast)
data('df_example')
kable(head(df_example)) 

The dataset is expected to have 3 types of columns:

• 1. A unit column: containing a numeric identification of the unit. In the credit card example this could the the customer, a group of customer, etc.,;
• 2. A time columns: containing the time in integer. In the credit card example this would be the age in months of the respective unit (say 1 for first month, 2 for the second month, etc.,);
• 3. Feature Columns: Numeric features, with both the serie(s) that will be forecasted as well as features to use to forecast. In the credit card this could be the profitability and transactional features.

The table bellow shows the max time for each unit:

library(dplyr)

df_example %>%
  group_by(unit) %>%
  summarise(max_time_period=max(time_period)) %>%
  filter(unit %in% c(1, 2, 3, 4, 5, 45, 46, 47, 48, 49, 50)) %>% 
  kable()

As one can see the older unit (the smaller the number the older the unit is) the longer is the time series that are available (larger values in the time_period column). This means that the data from older units can be used to forecast the younger units. For example, the data from units 18 to 1 could be used to predict the next 12 periods of the unit 30. This is excatly what the function run_synthetic_forecast() does (To better understand how it is working under the hood it is recommend to check the Synthetic Control Synth Package paper.).

The function call bellow runs a synthetic forecast of 12 time periods of the series x1 of the unit 30.

synthetic_forecast <- run_synthetic_forecast(
  df = df_example,
  col_unit_name = 'unit',
  col_time='time_period',
  periods_to_forecast=12,
  unit_of_interest = '30',
  serie_of_interest = 'x1'
)
#> [1] "Forecasting Unit:  30 . Serie:  x1"
#> 
#> X1, X0, Z1, Z0 all come directly from dataprep object.
#> 
#> 
#> **************** 
#>  searching for synthetic control unit  
#>  
#> 
#> **************** 
#> **************** 
#> **************** 
#> 
#> MSPE (LOSS V): 0.005105562 
#> 
#> solution.v:
#>  0.03795838 0.02953412 0.03356642 0.01533716 0.1226315 0.1285906 0.05816525 0.02318678 0.01465216 0.01080646 0.06187415 0.0289542 0.01702719 0.08006876 0.009607601 0.01627082 0.1278952 0.02615566 0.01342692 0.04431671 0.04468165 0.01097563 0.04431671 
#> 
#> solution.w:
#>  1.1452e-05 0.0002666085 0.0001873182 0.0002686277 0.0001636778 0.0003347625 0.0004905744 0.0005939929 0.0005203545 0.5502766 4.8739e-06 0.0007708196 0.0003844661 0.001002508 0.000803214 0.000999687 0.1285913 0.3143298

The output of the function is a list with 4 tables.

Synthetic Forecat Results

These are the 4 tables that are returned by the function call.

kable(synthetic_forecast$synthetic_control_composition)

kable(head(synthetic_forecast$variable_importance_and_comparison,8))

kable(synthetic_forecast$mape_backtest)

kable(synthetic_forecast$output_projecao)

proj<- synthetic_forecast$output_projecao
proj %>% glimpse()
#> Rows: 33
#> Columns: 6
#> $ execution_date        <chr> "2022-03-08", "2022-03-08", "2022-03-08", "2022-…
#> $ projected_unit        <chr> "30", "30", "30", "30", "30", "30", "30", "30", …
#> $ time_period           <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1…
#> $ projected_serie_value <dbl> 0.4354680, 0.4321821, 0.5256354, 0.4840789, 0.38…
#> $ is_projected          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ projected_serie       <chr> "x1", "x1", "x1", "x1", "x1", "x1", "x1", "x1", …