Chapter-03-Exercises.Rmd

---
title: "Chapter 03 Exercises"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(fpp3)
```

## 3.7 Exercises

1. Consider the GDP information in `global_economy`. Plot the GDP per capita for 
each country over time. Which country has the highest GDP per capita? 
How has this changed over time?
```{r}
global_GDP_per_cap <- global_economy %>%
  mutate(GDP_per_cap = GDP/Population) %>%
  select(Country, Year, GDP_per_cap) %>%
  group_by(Country)
  

autoplot(global_GDP_per_cap, GDP_per_cap) +
  theme(legend.position="none")
```
```{r}
global_GDP_per_cap %>%
  filter(Year == 2017) %>%
  arrange(desc(GDP_per_cap))

```

It appears Luxembourg has the highest GDP per cap. It appears they emerged as a 
leader in the early 80s and their GDP peaked around 2015.

***


2. For each of the following series, make a graph of the data. If transforming seems appropriate, do so and describe the effect.

* United States GDP from `global_economy`
```{r}
global_economy %>%
  mutate(GDP_per_cap = GDP/Population) %>%
  select(Country, Year, GDP, GDP_per_cap) %>%
  filter(Country == 'United States') %>%
  pivot_longer(c(GDP, GDP_per_cap),
               values_to = 'GDP') %>%
  ggplot(aes(x=Year, y=GDP)) +
  geom_line() +
  facet_grid(name ~ ., scales='free_y') +
  labs(y='US$',
       x='',
       title='GDP vs GDP Per Capita: USA')
  
```

I assumed that a population transformation would make sense here, but the graph did not 
change much besides the y-axis scale.


* Slaughter of Victorian “Bulls, bullocks and steers” in `aus_livestock`
```{r}
victorian_bulls <- aus_livestock %>%
  filter(Animal == 'Bulls, bullocks and steers' & State == 'Victoria')

autoplot(victorian_bulls,Count) +
  labs(y='Total Slaughtered',
      x='',
      title='Slaughter of Victorian Bulls, Bullocks, and Steers')

# box cox transformation

lambda <- victorian_bulls %>%
  features(Count, features = guerrero) %>%
  pull(lambda_guerrero)

victorian_bulls %>%
  autoplot(box_cox(Count, lambda)) +
  labs(y='Slaughtered Count (log)',
       x='',
       title='BoxCox Transform Slaughter of Victorian Bulls, Bullocks, and Steers')

```

Box-cox performed here to make the variation over time more consistent


* Victorian Electricity Demand from `vic_elec.`
```{r}
autoplot(vic_elec, Demand)


avg_elec_demand_daily <- aggregate(vic_elec['Demand'], by=vic_elec['Date'], mean)

avg_elec_demand_daily %>%
  as_tsibble(index=Date) %>%
  autoplot(Demand)
```

Transforming from half hour increments to the average daily makes the graph
more readable and the data easier to work with while retaining its shape. Could
also potentially use a box_cox transform

* Gas production from `aus_production`
```{r}
autoplot(aus_production, Gas)+
  labs(y='',
       title='Gas Production with No Tranform')

lambda <- aus_production %>%
  features(Gas, features = guerrero) %>%
  pull(lambda_guerrero)
aus_production %>%
  autoplot(box_cox(Gas, lambda)) +
  labs(y = "",
       title = latex2exp::TeX(paste0(
         "Transformed gas production with $\\lambda$ = ",
         round(lambda,2))))
```

Variance over time has been standardized with Box Cox tranform

***

3. Why is a Box-Cox transformation unhelpful for the `canadian_gas` data?
```{r}
autoplot(canadian_gas, Volume)

```

While the variance changes over time, variance does not increase consistently over
time with the increase in level. Variance increases and then decreases around 1991. 

***

4. What Box-Cox transformation would you select for your retail data (from Exercise 8 in Section 2.10

```{r}
set.seed(12345678)
myseries <- aus_retail %>%
  filter(`Series ID` == sample(aus_retail$`Series ID`,1))

autoplot(myseries, Turnover)


myseries %>%
  autoplot(box_cox(Turnover, lambda=0))
```

If I had to choose a lambda manually between 0 and 1, I would choose the natural
log or 0 for this series, though a power calculation using .1 also seems to make
the series more consistent.

***

5. For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance. Tobacco from `aus_production`, Economy class passengers between Melbourne and Sydney from `ansett`, and Pedestrian counts at Southern Cross Station from `pedestrian`.

I'm only going to be doing one of these since it is the same code for every box_cox

```{r}
autoplot(aus_production, Tobacco)

lambda <- aus_production %>%
  features(Tobacco, features = guerrero) %>%
  pull(lambda_guerrero)
aus_production %>%
  autoplot(box_cox(Tobacco, lambda)) +
  labs(y = "",
       title = latex2exp::TeX(paste0(
         "Transformed Tobacco Production with $\\lambda$ = ",
         round(lambda,2))))
```

***

6. Show that a 3×5MA is equivalent to a 7-term weighted moving average with weights of 0.067, 0.133, 0.200, 0.200, 0.200, 0.133, and 0.067.
```{r}
apple_stock <- gafa_stock %>%
  select(Adj_Close) %>%
  filter(Symbol == "AAPL" & year(Date) == 2014)
  
apple_stock %>%
  head(7) %>%
  mutate(
    `5-MA` = slider::slide_dbl(Adj_Close, mean, .before=2, .after=2, .complete=TRUE),
    `3x5-MA` = slider::slide_dbl(`5-MA`, mean, .before=1, .after=1, .complete=TRUE)
  )
  

```

This is how to construct a 3x5MA, but I'm unsure how to prove the weights.

***

7. Consider the last five years of the Gas data from `aus_production`.
    a. Plot the time series. Can you identify seasonal fluctuations and/or a trend-cycle?
    b. Use classical_decomposition with type=multiplicative to calculate the trend-cycle and seasonal indices.
    c. Do the results support the graphical interpretation from part a?
    d. Compute and plot the seasonally adjusted data.
    e. Change one observation to be an outlier (e.g., add 300 to one observation), and recompute the seasonally adjusted 
    f. data. What is the effect of the outlier?
    g. Does it make any difference if the outlier is near the end rather than in the middle of the time series?

```{r}
gas <- tail(aus_production, 5*4) %>% 
  select(Gas) # data is quarterly hence the suggested "5 X 4" for 5 yrs

autoplot(gas, Gas)
```

Seasonal fluctuation is pretty obvious here surging in the summer.


```{r}
# Classical multiplicative decomp
gas %>%
  model(
    classical_decomposition(Gas, type = 'multiplicative')
  ) %>%
  components() %>%
  autoplot() +
  labs(title = 'Classical Multiplicative Decomposition of Aus Gas Production')
```

Results of the decomp seem to support the conclusions for part a

```{r}
# plotting seasonally adjusted component
gas %>%
  model(
    classical_decomposition(Gas, type = 'multiplicative')
  ) %>%
  components() %>%
  ggplot(aes(x=Quarter)) +
  geom_line(aes(y = season_adjust,
                colour = "Seasonally Adjusted"))


```


```{r}
# adding 300 to an observation

gas$Gas[[10]] <- gas$Gas[[10]] + 300 
```

```{r}
gas %>%
  model(
    classical_decomposition(Gas, type = 'multiplicative')
  ) %>%
  components() %>%
  ggplot(aes(x=Quarter)) +
  geom_line(aes(y = season_adjust,
                colour = "Seasonally Adjusted"))
```

Outlier doesn't affect the seasonality as it is still visible once seasonally adjusted. 
It does not seem to matter where in the time series the outlier is. 

***

8. Recall your retail time series data (from Exercise 8 in Section 2.10). 
Decompose the series using X-11. Does it reveal any outliers, or unusual features 
that you had not noticed previously?
```{r}
set.seed(222)
myseries <- aus_retail %>%
  filter(`Series ID` == sample(aus_retail$`Series ID`,1))


myseries %>%
  model(x11 = X_13ARIMA_SEATS(Turnover ~ x11())) %>%
  components() %>%
  autoplot()

```

Decomposing by X-11 shows a change in the seasonality over time, but no other
outliers or unusual features that went unnoticed previously.

***

9. Figures 3.19 and 3.20 show the result of decomposing the number of persons in 
the civilian labour force in Australia each month from February 1978 to August 1995.
    a. Write about 3–5 sentences describing the results of the decomposition. Pay 
particular attention to the scales of the graphs in making your interpretation.
        * The scale of the decomp shows trend will likely have the most predictive power
as the seasonality will factor in very little. We can also see via the remainder
that there are macro forces affecting the data that cannot be accounted for in
the trend or seasonality.
    b. Is the recession of 1991/1992 visible in the estimated components?
        * Yes, it can be seen in the remainder.



***
10) This exercise uses the canadian_gas data (monthly Canadian gas production in 
billions of cubic metres, January 1960 – February 2005).
    a. Plot the data using autoplot(), gg_subseries() and gg_season() to look at the 
effect of the changing seasonality over time.1
    b. Do an STL decomposition of the data. You will need to choose a seasonal window 
to allow for the changing shape of the seasonal component.
    c. How does the seasonal shape change over time? [Hint: Try plotting the seasonal 
component using gg_season().]
    d. Can you produce a plausible seasonally adjusted series?
    e. Compare the results with those obtained using SEATS and X-11. How are they 
different?

```{r}
autoplot(canadian_gas, Volume)
gg_subseries(canadian_gas, Volume)
gg_season(canadian_gas, Volume)
```

Variance of the data fluctuates over time. Subseries plot shows lagging volume in
the 70's and 80s before continuing its upward trend.

```{r}
canadian_gas %>%
  model(STL(Volume ~ trend(window = 21) +
              season(window=12),
            robust = TRUE)) %>%
  components() %>%
  autoplot()
```

We can see seasonal shifts over time

```{r}
canadian_gas %>%
  model(STL(Volume ~ trend(window = 21) +
              season(window=12),
            robust = TRUE)) %>%
  components() %>%
  ggplot(aes(x=Month)) +
  geom_line(aes(y = season_adjust,
                colour = "Seasonally Adjusted"))
```

Above is a plausible seasonally adjusted series

```{r}
canadian_gas %>%
  model(x11 = X_13ARIMA_SEATS(Volume ~ x11())) %>%
  components() %>%
  autoplot()
```

```{r}
canadian_gas %>%
  model(seats = X_13ARIMA_SEATS(Volume ~ seats())) %>%
  components() %>%
  autoplot()
```

I think the X-11 does a better job than the SEATS or STL decomp. It picks up on
the fluctuation of seasonality and the residual appears to have less white noise.