---
title: "Comment by Ajay Mehta on \"Time Series Forecasting Lab (Part 2) - Feature Engineering with Recipes\" | Hashnode"
description: "An alternative approach to get standardization parameters would be to calculate them (again): tmp <- monthly_retail_tbl %>% group_by(Industry) %>% arra"
url: "https://hashnode.com/posts/time-series-forecasting-lab-part-2-feature-engineering-with-recipes/61d5c777e2f29f0805e13d7a/comment/61f0239f87fd2728e7cd3913"
---

[Discussion](https://hashnode.com/posts/time-series-forecasting-lab-part-2-feature-engineering-with-recipes/61d5c777e2f29f0805e13d7a)

Comment [Time Series Forecasting Lab (Part 2) - Feature Engineering with Recipes](https://hashnode.com/posts/time-series-forecasting-lab-part-2-feature-engineering-with-recipes/61d5c777e2f29f0805e13d7a)

[Jan 25, 2022](https://hashnode.com/posts/time-series-forecasting-lab-part-2-feature-engineering-with-recipes/61d5c777e2f29f0805e13d7a/comment/61f0239f87fd2728e7cd3913)

An alternative approach to get standardization parameters would be to calculate them (again):

```
tmp <- monthly_retail_tbl %>%
  group_by(Industry) %>% 
  arrange(Month) %>%
  mutate(Turnover = log1p(x = Turnover)) %>% 
  group_map(~ c(mean = mean(.x$Turnover, na.rm = TRUE),
                sd = sd(.x$Turnover, na.rm = TRUE))) %>% 
  bind_rows()

std_mean <- tmp$mean
std_sd <- tmp$sd
rm('tmp')
```