Module 3 Discussion

Module 3 Discussion

#to clean the whole environment
remove(list=ls())


#Set up all of the libraries needed
library(fpp3)

## Registered S3 method overwritten by 'tsibble':
##   method               from 
##   as_tibble.grouped_df dplyr

## ── Attaching packages ──────────────────────────────────────────── fpp3 1.0.2 ──

## ✔ tibble      3.3.0     ✔ tsibble     1.1.6
## ✔ dplyr       1.1.4     ✔ tsibbledata 0.4.1
## ✔ tidyr       1.3.1     ✔ feasts      0.4.2
## ✔ lubridate   1.9.4     ✔ fable       0.4.1
## ✔ ggplot2     3.5.2

## ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
## ✖ lubridate::date()    masks base::date()
## ✖ dplyr::filter()      masks stats::filter()
## ✖ tsibble::intersect() masks base::intersect()
## ✖ tsibble::interval()  masks lubridate::interval()
## ✖ dplyr::lag()         masks stats::lag()
## ✖ tsibble::setdiff()   masks base::setdiff()
## ✖ tsibble::union()     masks base::union()

library(fredr)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0     ✔ readr   2.1.5
## ✔ purrr   1.1.0     ✔ stringr 1.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter()     masks stats::filter()
## ✖ tsibble::interval() masks lubridate::interval()
## ✖ dplyr::lag()        masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(patchwork)
library(knitr)
library(kableExtra)

## 
## Attaching package: 'kableExtra'
## 
## The following object is masked from 'package:dplyr':
## 
##     group_rows

library(writexl)
library(dplyr)
library(feasts)

#Set up my FRED Key
#430508d0ad1df25ecddc34d5581dafc8

fredr_set_key("430508d0ad1df25ecddc34d5581dafc8")

#Pulling out the data and setting is as time series data 


#A CPI Data
cpi <- fredr("CPIAUCSL",
              observation_start = as.Date("2000-01-01"),
              observation_end   = as.Date("2025-08-01")
              ) |>
  mutate(Month = yearmonth(date), value) |>
  as_tsibble(index = Month) 

#checking the data 
autoplot(cpi, value)

any(is.na(cpi))

## [1] FALSE

is_tibble(cpi)

## [1] TRUE

##ACF and PCF
ACF(cpi) |> 
autoplot()

## Response variable not specified, automatically selected `var = date`

#Strong trend, not seasonal

PACF(cpi) |> 
  autoplot()

## Response variable not specified, automatically selected `var = date`

#AR(1) model could effectively model the data.

cpi |>
  model(
    classical_decomposition(value, type = "additive")
  ) |>
  components() |>
  autoplot() +
  labs(title = "Consumer Price Index")

## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_line()`).

#B 30-Year Mortgage Rate Data
#MORTGAGE30US
mor30 <- fredr("MORTGAGE30US",
              observation_start = as.Date("1971-04-02"),
              observation_end   = as.Date("2025-09-18")
              ) |>
  mutate(Week = yearweek(date), value) |>
  as_tsibble(index = Week) 


#checking the data 
autoplot(mor30, value)

any(is.na(mor30))

## [1] FALSE

is_tibble(mor30)

## [1] TRUE

##ACF and PCF
ACF(mor30) |> 
autoplot()

## Response variable not specified, automatically selected `var = date`

#Strong trend, not seasonal.

PACF(mor30) |> 
  autoplot()

## Response variable not specified, automatically selected `var = date`

#AR(1) model could effectively model the data.

#Classical Decomposition 
mor30 |>
  model(
    classical_decomposition(value, type = "additive")
  ) |>
  components() |>
  autoplot() +
  labs(title = "30-Year Mortgage Rate")

## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_line()`).

#C Cost of a Dozen Eggs Data
#APU0000708111
egg <- fredr("APU0000708111",
              observation_start = as.Date("1980-01-01"),
              observation_end   = as.Date("2025-08-01")
              ) |>
  mutate(Month = yearmonth(date), value) |>
  as_tsibble(index = Month)


#checking the data 
autoplot(egg, value)

any(is.na(egg))

## [1] FALSE

is_tibble(egg)

## [1] TRUE

##ACF and PCF
ACF(egg) |> 
autoplot()

## Response variable not specified, automatically selected `var = date`

#Strong trend, not seasonal.

PACF(egg) |> 
  autoplot()

## Response variable not specified, automatically selected `var = date`

#AR(1) model could effectively model the data.

#Classical Decomposition 
egg |>
  model(
    classical_decomposition(value, type = "additive")
  ) |>
  components() |>
  autoplot() +
  labs(title = "US Egg Cost")

## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_line()`).