Draft

1. Forecasting models based on decomposition methods
2. Adding ETS and ARIMA filters
- 2.1 Exponential Smoothing
- 2.2 Identifying Stationarity
- 2.3 ARIMA models
- 2.4 Decomposition & ETS/ARIMA
3. Adding exogenous variables to the model
- 3.1 Linear Regression Models
- 3.2 Build dynamic regressions
- 3.3 Analyze the model's performance
- 3.4 Choose the best variables for the model
4. Forecasting at scale
- 4.1 Understanding the challenges and approaches when dealing with multiple time series
- 4.2 Applying hierarchical and reconciliation models
- 4.3 Efficient forecasting workflow
- 4.4 Evaluation of the global performance

Forecasting workflow

Author

pbenavides

Modified

September 4, 2025

1 H1

forecasting

forecasting again

library(tsibble)
library(feasts)
library(fable)

library(timetk)
library(modeltime)

data_tsbl |> 
  index_by() |> 
  summarise()

data_tbl |> 
  summarise_by_time()