46-exercises.Rmd

---
title: "Forecasting: Principles and Practice Chapter 4 Exercises"
author: "Neil Martin"
date: "2024-09-04"
output: html_document
---

```{r setup, include=FALSE}
library(fpp3)
library(knitr)
library(ggplot2)
library(readxl)
library(dplyr)
library(readr)
library(seasonal)
library(latex2exp)
library(tsibble)
library(feasts)
library(tidyr)
```
#### Write a function to compute the mean and standard deviation of a time series, and apply it to the PBS data. Plot the series with the highest mean, and the series with the lowest standard deviation.
```{r mean_standard_dev}

PBS

meanstd <- function(x) {
  c(
    mean = mean(x, na.rm = TRUE),
    sd = sd(x, na.rm = TRUE)
  )
}

pbs_features <- PBS |>
  features(Cost, features = meanstd)

pbs_features

pbs_features |>
  filter(mean == max(mean))

pbs_features |>
  filter(sd == min(sd))
```
#### Use GGally::ggpairs() to look at the relationships between the STL-based features for the holiday series in the tourism data. Change seasonal_peak_year and seasonal_trough_year to factors, as shown in Figure 4.3. Which is the peak quarter for holidays in each state?

```{r tourism_cont}

tourism |>
  features(Trips, feat_stl) |>
  select(-Region, -State, -Purpose) |>
  mutate(
    seasonal_peak_year = factor(seasonal_peak_year),
    seasonal_trough_year = factor(seasonal_trough_year),
  ) |>
  GGally::ggpairs()

tourism %>%
  group_by(State) %>%
  summarise(Trips = sum(Trips)) %>%
  features(Trips, feat_stl) %>%
  select(State, seasonal_peak_year)
```   

#### Personally found the last chapter incredibly confusing.