Cnvenient way in R is to define a time series objects two important things to specify the starting period,frequency

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Time Series Graphics 

1.     It is always a good idea in Time Series analysis to plot the data first to see how data change over time.

2.     To do that, a convenient way in R is to define a time series objects (ts). Two important things to specify: (1) the starting period; (2) frequency. Examples: 

i. Annual data starts at 2012.

y <- ts(c(123,39,78,52,110), start=2012)                                                                                                  

ii. Monthly data starts at January 2003.

In the above examples, c(.) is a made up series. When dealing with actual data, just replace c(.) with the name of your data. When we are using the data in fpp2, the data is specified as time series object already, so we can skip this step.

3.     The most common plotting function we use in this class is autoplot. Example:

In the above program, we plot the data in a10, define the title of the figure as

"Antidiabetic drug sales", label y axis as $million and x axis as Year.

4.     There are three patterns that we want to identify in time series.

a.     Trend: a long-term increase or decrease in the data

b.     Seasonal: when a time series is affected by seasonal factors such as the time of the year or the day of the week.

c.      Cyclic: when the data exhibit rises and falls that are not of a fixed frequency.

5.     To identify seasonal pattern, two useful plots can be used: ggseasonplot and ggsubseriesplot. Examples:

Try to apply the above two programs. Can you identify seasonal pattern based on these

plots?

6.     Two other very important plots in identifying time series patterns are the lag plot and autocorrelation plot. Example of lag plot:

Attachments:

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