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Monash University - ETF 5231Chapter 5-Solution

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Chapter 5: Exercises & Solutions ETF3231/5231 Hide library(fpp3) fpp 5.10, ex1 Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appro ... priate in each case:  Australian Population (global_economy)  Bricks (aus_production)  NSW Lambs (aus_livestock) Australian population Hide global_economy %>% filter(Country == "Australia") %>% autoplot(Population)Data has trend and no seasonality. Random walk with drift model is appropriate. Hide global_economy %>% filter(Country == "Australia") %>% model(RW(Population ~ drift())) %>% forecast(h = "10 years") %>% autoplot(global_economy)Australian clay brick production Hide aus_production %>% filter(!is.na(Bricks)) %>% autoplot(Bricks) + ggtitle("Clay brick production")This data appears to have more seasonality than trend, so of the models available, seasonal naive is most appropriate. Hide aus_production %>% filter(!is.na(Bricks)) %>% model(SNAIVE(Bricks)) %>% forecast(h = "5 years") %>% autoplot(aus_production)NSW Lambs Hide nsw_lambs <- aus_livestock %>% filter(State == "New South Wales", Animal == "Lambs") nsw_lambs %>% autoplot(Count) [Show More]

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