Untitled
Heleine Fouda
2024-09-28
knitr::opts_chunk$set(echo = TRUE)
library(fpp3)## Warning: package 'fpp3' was built under R version 4.3.3## Registered S3 method overwritten by 'tsibble':
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library(tsibble)
library(ggplot2)
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(fpp3)
library(USgas)
library(tsibble)
library(ggplot2)
library(tidyverse)
library(fable)
library(tsibble)
library(fabletools)
library(fable)
library(dplyr)R Markdown
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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
aus_economy <- global_economy |>
filter(Code == "AUS") |>
mutate(Pop = Population / 1e6)
autoplot(aus_economy, Pop) +
labs(y = "Millions", title = "Australian population")
Including Plots
algeria_economy <- global_economy |>
filter(Country == "Algeria")
algeria_economy |>
autoplot(Exports) +
labs(y = "% of GDP", title = "Exports: Algeria")
aus_economy <- global_economy |>
filter(Code == "AUS") |>
mutate(Pop = Population / 1e6)
autoplot(aus_economy, Pop) +
labs(y = "Millions", title = "Australian population")
library(fable)
library(dplyr)
# Fit Holt’s linear trend model to the Australian population data
fit <- aus_economy |>
model(
Holt = ETS(Pop ~ error("A") + trend("A") + season("N"))
)
# Generate the 10-year forecast
fc <- fit |> forecast(h = 10)
# Check the structure of the forecast object
# This will display the column names and a preview of the data
print(fc)## # A fable: 10 x 5 [1Y]
## # Key: Country, .model [1]
## Country .model Year Pop .mean
## <fct> <chr> <dbl> <dist> <dbl>
## 1 Australia Holt 2018 N(25, 0.0041) 25.0
## 2 Australia Holt 2019 N(25, 0.011) 25.3
## 3 Australia Holt 2020 N(26, 0.023) 25.7
## 4 Australia Holt 2021 N(26, 0.039) 26.1
## 5 Australia Holt 2022 N(26, 0.061) 26.4
## 6 Australia Holt 2023 N(27, 0.09) 26.8
## 7 Australia Holt 2024 N(27, 0.13) 27.2
## 8 Australia Holt 2025 N(28, 0.17) 27.6
## 9 Australia Holt 2026 N(28, 0.22) 27.9
## 10 Australia Holt 2027 N(28, 0.29) 28.3fit <- aus_economy |>
model(
AAN = ETS(Pop ~ error("A") + trend("A") + season("N"))
)
fc <- fit |> forecast(h = 10)
fc## # A fable: 10 x 5 [1Y]
## # Key: Country, .model [1]
## Country .model Year Pop .mean
## <fct> <chr> <dbl> <dist> <dbl>
## 1 Australia AAN 2018 N(25, 0.0041) 25.0
## 2 Australia AAN 2019 N(25, 0.011) 25.3
## 3 Australia AAN 2020 N(26, 0.023) 25.7
## 4 Australia AAN 2021 N(26, 0.039) 26.1
## 5 Australia AAN 2022 N(26, 0.061) 26.4
## 6 Australia AAN 2023 N(27, 0.09) 26.8
## 7 Australia AAN 2024 N(27, 0.13) 27.2
## 8 Australia AAN 2025 N(28, 0.17) 27.6
## 9 Australia AAN 2026 N(28, 0.22) 27.9
## 10 Australia AAN 2027 N(28, 0.29) 28.3www_usage <- as_tsibble(WWWusage)
www_usage |> autoplot(value) +
labs(x="Minute", y="Number of users",
title = "Internet usage per minute")
aus_economy |>
model(
`Holt's method` = ETS(Pop ~ error("A") +
trend("A") + season("N")),
`Damped Holt's method` = ETS(Pop ~ error("A") +
trend("Ad", phi = 0.9) + season("N"))
) |>
forecast(h = 15) |>
autoplot(aus_economy, level = NULL) +
labs(title = "Australian population",
y = "Millions") +
guides(colour = guide_legend(title = "Forecast"))
www_usage <- as_tsibble(WWWusage)
www_usage |>
stretch_tsibble(.init = 10) |>
model(
SES = ETS(value ~ error("A") + trend("N") + season("N")),
Holt = ETS(value ~ error("A") + trend("A") + season("N")),
Damped = ETS(value ~ error("A") + trend("Ad") +
season("N"))
) |>
forecast(h = 1) |>
accuracy(www_usage)## Warning: The future dataset is incomplete, incomplete out-of-sample data will be treated as missing.
## 1 observation is missing at 101## # A tibble: 3 × 10
## .model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Damped Test 0.288 3.69 3.00 0.347 2.26 0.663 0.636 0.336
## 2 Holt Test 0.0610 3.87 3.17 0.244 2.38 0.701 0.668 0.296
## 3 SES Test 1.46 6.05 4.81 0.904 3.55 1.06 1.04 0.803