Forecasting 3
simplymathematics
February 24, 2019
library(fpp2)## Loading required package: ggplot2## Loading required package: forecast## Loading required package: fma## Loading required package: expsmoothlibrary(seasonal)Problem 6.2
a
Plastic sales tend to peak in the middle of the month. ### b
### c The decomposition shows the same monthly trend as well as a general upward tick in production (excluding the last month). ### d
## Warning: Removed 12 rows containing missing values (geom_path).
### e
## Warning: Removed 12 rows containing missing values (geom_path).
The outlier effects the seasonally adjusted value greatly, but does not greatly effect the trend line. ### f
## Warning: Removed 12 rows containing missing values (geom_path).
The later in the data the outlier is, the less it effects the trend line. ## Problem 6.3 ### Load Data
## readxl works best with a newer version of the tibble package.
## You currently have tibble v1.4.2.
## Falling back to column name repair from tibble <= v1.4.2.
## Message displays once per session.
Decompose the series using X11
As expected, the biggest outlier to air traffic was 2001. Additionally, there has been recent volatility perhaps due to fluctuating oil prices and other travel-related concerns.