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visnights contains quarterly visitor nights (in millions) from 1998 to 2016 for twenty regions of Australia. a. Use window( ) to create three training sets for visnights [, "QLDMetro"], omitting the last 1, 2 and 3 years; call these train1, train2, and train3, respectively. For example train1 <- window(visnights[, "QLDMetro"], end $=c(2015,4))$. b. Compute one year of forecasts for each training set using the snaive() method. Call these $f c 1, f c 2$ and $f c 3$, respectively. c. Use accuracy() to compare the MAPE over the three test sets. Comment on these.

   visnights contains quarterly visitor nights (in millions) from 1998 to 2016 for twenty regions of Australia.
a. Use window( ) to create three training sets for visnights [, "QLDMetro"], omitting the last 1, 2 and 3 years; call these train1, train2, and train3, respectively. For example train1 <- window(visnights[, "QLDMetro"], end $=c(2015,4))$.
b. Compute one year of forecasts for each training set using the snaive() method. Call these $f c 1, f c 2$ and $f c 3$, respectively.
c. Use accuracy() to compare the MAPE over the three test sets. Comment on these.
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Forecasting: Principles and Practice
Forecasting: Principles and Practice
George… 2nd Edition
Chapter 3, Problem 9 ↓

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visnights contains quarterly visitor nights (in millions) from 1998 to 2016 for twenty regions of Australia. a. Use window( ) to create three training sets for visnights [, "QLDMetro"], omitting the last 1, 2 and 3 years; call these train1, train2, and train3, respectively. For example train1 <- window(visnights[, "QLDMetro"], end $=c(2015,4))$. b. Compute one year of forecasts for each training set using the snaive() method. Call these $f c 1, f c 2$ and $f c 3$, respectively. c. Use accuracy() to compare the MAPE over the three test sets. Comment on these.
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