library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.1
## ✔ readr   2.1.2     ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(openxlsx)
library(lubridate)
## 
## Attaching package: 'lubridate'
## 
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(readxl)
library(tsibble)
## 
## Attaching package: 'tsibble'
## 
## The following object is masked from 'package:lubridate':
## 
##     interval
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, union
library(dplyr)
library(tsibbledata)
library(tidyverse)
library(fable)
## Loading required package: fabletools
library(ggplot2)

gas <- readr::read_csv("C:/Users/ryanf/Downloads/gas.csv")
## Rows: 1541 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): date
## dbl (1): price
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
gas_ts <- as_tibble(gas)

gas_ts
## # A tibble: 1,541 × 2
##    date      price
##    <chr>     <dbl>
##  1 5-Apr-93   1.07
##  2 12-Apr-93  1.08
##  3 19-Apr-93  1.08
##  4 26-Apr-93  1.09
##  5 3-May-93   1.09
##  6 10-May-93  1.10
##  7 17-May-93  1.11
##  8 24-May-93  1.11
##  9 31-May-93  1.11
## 10 7-Jun-93   1.10
## # … with 1,531 more rows
 # index=date
#gas_fit <- gas %>%
 # model(
  #  Seasonal_naive = SNAIVE(price),
 #   Naive = NAIVE(price),
 #   Drift = RW(price ~ drift()),
 #   Mean = MEAN(price)
#  )

Hey Alex, it may not look like it, but I have spent the last few hours working on this.Spent a lot of time fooling around with data trying to get it imported properly, and now I can not get my date column to show up as my index(at least I think that is the problem) and as a result I cannot get the data properly formatted in tsibble to do other things. Normally, when I get behind in a class I have no problem teaching myself very quickly, but this class appears to be the exception. It is a lot of work, very technical, and hard to diagnose where I am going wrong with vague error messages. Since this strategy does not appear to be working for me I am going to change it up for the future. I am sorry I waited until this last minute to do this assignment but life has been very demanding as of late.Please dont give up on me because I promise I am trying.

I know I did not complete the majority of this assignment, but I will tell you my plan for whats its worth. I planned on modeling the data and forecasting using all four methods. I would consider all the forecasts, but I expected to choose the SNAIVE method because I think gas can be very seasonal, but also has fluctuating trends. I didnt really have the oppourtunity for thinking beyond that. Anyways, thanks for hearing me out. I plan on being in your office hours if I can make it before homework 4 is due.