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Everything was great, until Midas touched his daughter, and she turned to gold. King Midas wanted everything he touched to turn to gold. So, he killed the goose to get the eggs (which means that the goose laid no more eggs). But, the goose only laid one egg a day, and the farmer wanted more eggs. Moral: Don't believe a flatterer/Don't be a showoff.Ī farmer had a goose that laid golden eggs, so he sold them and became rich. The crow was flattered and began to sing. The fox began to give the crow compliments about her singing, asking her to sing. In the summer, the ant is busy working, storing food for the winter.

Along the way, the hare is far enough ahead to take a nap, and the tortoise passes him, winning the race. The crow had a piece of cheese, and the fox wanted it. Examples of Fable: The tortoise and the hare have a race because the hare is bragging about how fast he is.

When winter comes, the ant is prepared and the grasshopper is not. These short tales were once passed down as folklore to teach listeners the difference between right and wrong, give advice on proper behavior and manners, and offer maxims to live by. The grasshopper does not work-only plays. Writing How to Write a Fable in 5 Steps Written by MasterClass Last updated: 3 min read Fables are characterized by their moral lessons. YouĬan use this to ask for help if the question isn’t already answered.The tortoise and the hare have a race because the hare is bragging about how fast he is. Questions about forecasting can be asked on CrossĬommon questions about the fable package are often found on Stack Overflow. Introduction to time series forecasting using fable:

The forecasting principles and practices online textbook provides an.

The pkgdown site describes all models provided by fable, and how.

Library(fable) library(tsibble) library(tsibbledata) library(lubridate) library(dplyr) aus_retail %>% filter( State %in% c( "New South Wales", "Victoria"), Industry = "Department stores" ) %>% model( ets = ETS( box_cox(Turnover, 0.3)), arima = ARIMA( log(Turnover)), snaive = SNAIVE(Turnover) ) %>% forecast( h = "2 years") %>% autoplot( filter(aus_retail, year(Month) > 2010), level = NULL)